*Review* **Building Information Modelling and Internet of Things Integration for Facility Management—Literature Review and Future Needs**

**Antonino Mannino \* , Mario Claudio Dejaco and Fulvio Re Cecconi**

Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133 Milano, Italy; mario.dejaco@polimi.it (M.C.D.); fulvio.rececconi@polimi.it (F.R.C.) **\*** Correspondence: antonino.mannino@polimi.it

**Abstract:** Digitisation of the built environment is seen as a significant factor for innovation in the Architecture, Engineering, Construction and Operation sector. However, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Management. Therefore, optimisation of data collection and management is needed, all the more so now that Industry 4.0 has widened the use of sensors into buildings and infrastructures. A literature review on the two main pillars of digitalisation in construction, Building Information Modelling and Internet of Things, is presented, along with a bibliographic analysis of two citations and abstracts databases focusing on the operations stage. The bibliographic research has been carried out using Web of Science and Scopus databases. The article is aimed at providing a detailed analysis of BIM–IoT integration for Facility Management (FM) process improvements. Issues, opportunities and areas where further research efforts are required are outlined. Finally, four key areas of further research development in FM management have been proposed, focusing on optimising data collection and management.

**Keywords:** Building Information Modelling (BIM); Internet of Things (IoT); facility management; cyber-physical systems; digital twin

#### **1. Introduction**

The construction industry has a relatively low digitisation level compared to other sectors [1,2]. Although it is seen as a major factor in the innovation of the Architecture, Engineering and Construction and Operations (AECO) sector, digitisation in the construction industry still shows a slow growth rate [3]. However, improvements in methodologies and technologies are under development to better manage AECO processes [4].

This article presents a literature review on the integration of Building Information Modelling (BIM) and Internet of Things (IoT) for the Facility Management (FM) of the constructed asset. It is divided into four main parts: (a) an overview of FM and the impact of digitisation in the sector; (b) the description of the research method; (c) an in-depth content analysis of 99 selected journals' articles on BIM-IOT integration for FM, which allows identifying both benefits/opportunities and issues/limits at technical and operational levels; (d) conclusions and a description of a possible future research agenda.

The context is the fourth industrial revolution (Industry 4.0), where several technological changes in many sectors have been made, including in the AECO one [5,6].

There are many studies on the application of digital technologies aiming to promote digitisation in the built environment. However, compared to the design and construction stages, there is a lack of research on applying these new technologies in the operation and use stage of the building life cycle [7], particularly for the FM sector. FM represents up to 85% of the whole life cycle cost of the building [8]. Even though the life cycle cost of a building can and should be controlled in the design phase the adoption of innovative

**Citation:** Mannino, A.; Dejaco, M.C.; Re Cecconi, F. Building Information Modelling and Internet of Things Integration for Facility Management— Literature Review and Future Needs. *Appl. Sci.* **2021**, *11*, 3062. https:// doi.org/10.3390/app11073062

Academic Editor: Jürgen Reichardt

Received: 27 February 2021 Accepted: 25 March 2021 Published: 30 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

tools and technologies to improve FM in existing buildings is continually increasing. Wong et al. [7] identified and discussed several possibilities for future research into digital technologies like integrating FM with BIM, reality capture technology, IoT, Radio Frequency Identification (RFID), and Geographic Information System.

Among several studies on applying new technologies, a significant solution taken into consideration in the last years by the AECO sector has emerged: the Cyber-Physical Systems (CPS) [6]. CPS, also known as Digital Twins (DT), are systems based on the combination of physical and digital objects. Through simulation of an as-built component (or system), using digital models and several types of data, DT allows mirroring the life of its corresponding real twin to forecast the health of building components, their service life, faults [9] and, in general, the building performances [10].

Even if not risk-free, these digital innovations will enable new dynamics and allow new services that will improve efficiency and sustainability in building management processes [11].

#### *1.1. Facility Management*

Facility management is a multidisciplinary topic that requires the collaboration and coordination of different people [6]. ISO 41011:2017 defines FM as an "organisational function which integrates people, place and process within the built environment with the purpose of improving the quality of life of people and the productivity of the core business" [12].

According to International Facility Management Association (IFMA), there are 11 core competencies in FM [13]: Occupancy and Human Factors, Operations and Maintenance (O&M), Sustainability, Facility Information and Technology Management, Risk Management, Communication, Performance and Quality, Leadership and Strategy, Real Estate, Project Management, Finance and Business.

Currently, not all buildings have optimal management [14,15] due to outdated procedures that cause a lack of data and information. In other cases, despite the use of sensors/automatic devices and databases, the information collected is not entirely exploited [16]. An example is given by FM information systems, e.g., Computerised Maintenance Management Systems (CMMS), Energy Management Systems (EMS) and Building Automation Systems (BAS), where data are often fragmented and manually entered after the handover of the building. Fragmentation and data poorness could generate laborious and inefficient processes [7]. Furthermore, FM operators often rely on paper documents in their daily activities. This increases both the time needed and the difficulties of getting accurate information [17]. For these reasons, the improvement of both FM tools and processes is a crucial issue in FM companies [18]. Hence, with increasing industry interest, a review of the current status and a description of a future research agenda on FM is needed.

#### *1.2. Digitisation and FM*

New technologies have transformed many people's daily lives and have revolutionised several traditional industry practices aiming to achieve efficiency, accuracy, and precision. This evolution has gained momentum due to advancements in technologies such as the Internet of Things (IoT), big data, cloud computing and cyber-physical systems [19].

The strengths of these innovations 4.0 lie in monitoring, controlling, interoperability, real-time information processing and process self-optimisation [19]. The physical world's connection with the virtual world enables products and components to create a selfadapting and self-managing communication network [20].

In the construction sector, the first attempt at digitisation aiming to increase the sector's efficiency has already been seen with the spread of BIM [21].

#### *1.3. Building Information Modelling for FM*

The United States National Institute of Building Sciences (NIBS) defines BIM as "The digital representation of physical and functional characteristics of a facility. As such, it serves as a shared knowledge resource for information about a facility, forming a reliable basis for decisions during its life cycle from inception onwards" [22].

In recent years, BIM has been more and more employed in the AECO sector to improve information management. BIM Models (BIMs) allow integrated management of information throughout the building's entire life cycle, hence improving FM [23]. On the one hand, BIM allows working more efficiently during the design and construction phases by developing a 3D model that avoids project interference and allows project time and cost calculation. On the other hand, it allows acquiring data created during several phases of the building life cycle to use them in operations management, maintenance activities, environmental analysis and energy performance simulations. The latter is related to Building Energy Modelling (BEM), which has become an essential aspect for FM.

Benefits of using BIM in FM include providing "as-is" information and enabling Facility Managers to work on information using a single source of data, overcoming all the issues deriving from the sources' fragmentation.

A BIM model has different Levels of Information Needs [24]. To deal with them, the American BIMForum defined the Level of Development (LOD) Specification. This reference enables practitioners in the AEC Industry to specify and articulate with a high level of clarity the content and reliability of Building Information Models (BIMs) at various stages in the design and construction process. A BIM model has six Levels Of Development (LOD): LOD 100, LOD 200, LOD 300, LOD 350, LOD 400 and LOD 500 [25]. Each LOD defines how much information is included in a building component. The higher the LOD, the greater the clarity and reliability of data and information.

According to Love et al. [26], using the highest LOD is possible in order to enrich the digital model with all the information necessary for assets management and maintenance. In this way, data are more efficiently stored in a single file without fragmentation or loss of information. Moreover, improving the handover process is possible using fewer paper documents or manual transfer of information [26].

As early as 2012, Becerik-Gerber et al. [27] have defined, also through surveys and interviews, how BIM can support FM practices. Their paper assesses the status of BIM implementations in FM, potential applications, level of interest in BIM utilisation, application areas, and data requirements for BIM-enabled FM practices. To date, studies on BIM application in FM confirm momentum (e.g., [18,28–33]).

However, despite all the advantages, BIM is not often used in the FM phase. The most significant causes that hinder this integration are:


#### *1.4. Internet of Things for FM*

Asghari, Rahmani and Javadi [38] define IoT as "an ecosystem that contains smart objects equipped with sensors, networking and processing technologies integrating and working together to provide an environment in which smart services are taken to the end-users". They show how this ecosystem is being applied in healthcare, environmental, smart cities, commercial and industrial contexts. IoT has led to an interconnection between people and objects at an unprecedented scale and pace [39] and will allow new strategies

to improve quality of life [40]. Furthermore, connected devices could be programmed to make autonomous decisions and adequately inform users to make the best decisions [41].

Operation and maintenance stages represent 50–70% of the total annual facility operating costs [42], and buildings management requires integrating and analysing different types of data and information generated by various stakeholders. This implies that improved data and information management can have a significant impact on building performance.

In this context, the application and integration of IoT and BIM technologies to gather and store data/information for the entire life cycle of the building have caught wide attention. In recent years, a growing number of innovations have been developed [7].

IoT and smart connection have great potential in optimising FM activities, including inventory and document management, building security, logistics and materials tracking, tracking of building component life cycle and building energy controls [7]. Several studies about the use of data coming from IoT devices have been carried out (e.g., [43–45]), although many of them do not include the integration of BIM.

#### **2. Scope and Aim of the Research**

As mentioned before, BIM and IoT- based data sources is a relatively new field. One can consider BIM and IoT data as two complementary entities, where one covers the lack of the other. Researchers have addressed different aspects of BIM, IoT and their use in an integrated way: sustainability, risks, safety and so forth.

In this article, integration is addressed more from the point of view of the information collected/transmitted by sensors and actuators (and used for a specific purpose) than from that of the software or platform used. Hence, studies and research published on BIM and IoT data integration are analysed in this paper. The content is structured as a bibliographic investigation through which an analysis of these technologies' current use is carried out. The aims of this research are:


#### **3. Materials and Methods**

This study analyses and categorises existing studies on BIM and IoT integration for FM according to the methodology shown in Figure 1. To review BIM-IoT integration comprehensively in the Facility Management context, two electronic databases of peerreviewed literature have been taken into consideration: Scopus and Web of Science (WoS). The bibliometric analysis presented here aims to analyse academic publications and trends to evaluate the existing research performance and understand patterns. As the first step, keywords to select articles on BIM-IoT integration for FM functions are defined. Table 1 highlights the keywords used to find publications on BIM and IoT. Table 2 shows the set of keywords for each FM core competence.

**Table 1.** Keywords used for research in the two electronic databases of peer-reviewed literature Scopus and Web of Science (WoS). The asterisk "\*" after the keywords tells the search engine to look for all the words beginning with that keywords, i.e., "sensor\*" tells the search engine to look for the words "sensor", "sensors", "sensoring", etc . . . The quotations marks surrounding two or more words tell the search engine to look for the phrase and not the words, i.e., "industry foundation classes" is use to search for the prhase and not for the words industry or foundation or classes.


**Figure 1.** Research methodology.

After the keywords selection, Scopus and WoS databases were queried, using the keywords shown in Table 1, to find publications dealing with: (a) BIM; (b) IoT; and (c) BIM and IoT at the same time. This first-level query investigated how much these topics have been explored by researchers, even outside the FM field.

In a second-level query on the two databases, BIM and IoT keywords (Table 1) were coupled with FM core competencies (Table 2) to measure how deep BIM and IoT permeate FM core competencies.


**Table 2.** FM and its core competencies keywords used for research in the two electronic databases of peer-reviewed literature Scopus and Web of Science (WoS).

A set of filters was applied to the various searches to limit the large number of results. To perform this selection in the WoS database, results were as follows:


Articles from "computer science interdisciplinary applications" and "engineering multidisciplinary" domains were included in the review to ensure a comprehensive review of BIM and IoT device integration. Finally, the results were further filtered (by title, abstract and author's keywords) to remove articles not relevant for the research scope.

To perform a similar selection in the Scopus database, results were as follows:


As the last step, the two search results were combined, and duplicated articles were excluded. Finally, a list of 99 articles on the BIM-IOT integration was selected. Five out of ninety-nine articles are general reviews on BIM-IOT for FM and were already discussed in the introduction. To derive patterns and propose future research directions, qualitative data analyses of the 94 articles based on each article's technical aspect were carried out, as discussed in Section 5.

#### **4. Results**

The first result of the query using the keywords combination method explained in Section 3 shows a fairly clear gap between the number of publications on the three main topics. More than 95% of the articles deal with IoT (Table 3). A limited number of publications, less than 4%, of articles deal with BIM. Lastly, the integration of BIM and IoT is still at an early stage.

**Table 3.** Number of journal articles on BIM, IoT and their integration resulting from Scopus and WOS databases research (until February 2021).


Considering the number of publications on BIM and IoT integration in the last 30 years, it is possible to see how the first significant increase is registered after 2013 (Figure 2). Furthermore, publications grew almost simultaneously in both citation databases. Interestingly, 80% of the articles on Scopus and 85% of the articles on WoS were published during the last five years; this means that BIM-IoT integration is a new domain with increasing interest, especially during 2019 and 2020. Accordingly, the implementation of BIM-IoT integration for FM is also a new domain, with a limited number of publications (red line in Figure 2).

**Figure 2.** Number of publications per year (\* until February 2021) dealing with BIM and IoT in Scopus (yellow dotted), in Web of Science (blue dash-dotted) and number of product dealing with BIM and IoT for Facility Management (red dashed).

Journals' articles on BIM, IoT and their integration for FM and its core functions from

limited to journals' articles: Table 4 shows how many products

'

A further query using FM core functions keywords (Table 2) was carried out to find as many articles as possible on BIM and IoT integration in the FM field. Results are shown in Table 4.


**Table 4.** Journals' articles on BIM, IoT and their integration for FM and its core functions from WoS (W) and Scopus (S) databases (until February 2021).

The query was limited to journals' articles: Table 4 shows how many products were found in each database matching each FM core function with the three tools' categories.

Although the contemporary use of BIM and IoT is relatively recent, some FM core functions like "Sustainability", "O&M", "Communication", and "Technology" have a significant number of publications. On the contrary, perhaps because of the novelty of the two tools' simultaneous use, some functions have a minimal number of publications. Noteworthy, "Sustainability" is the most studied core function even when considering BIM or IoT separately. The number of publications in the several FM core functions is relatively homogeneous if only BIM products are queried.

To narrow down the scope of the review, further analysis was done on the title and abstract of each of the 904 articles dealing with BIM and IoT, discarding articles not directly related to the construction sector and FM (Table 5).

Eventually, duplicates, i.e., articles covering more than one core function or present in both databases, were discarded, and the final list of 99 articles emerged. On these 99 articles published between 2013 and 2021 (Table 6), a bibliometric analysis was carried out using the R package Bibliometrix [46].

Over the period under review, and based on the proposed selection criteria, most of the articles on BIM-IoT integration for FM were published in *Automation in Construction*, with 27 of the total selected articles. Followed by: *Applied Sciences* (6), *Journal of Computing in Civil Engineering* (5), *Sustainability* (5), *Advanced Engineering Informatics* (3), *Building and Environment* (3), *Journal of Construction Engineering and Management* (3) and *Journal of Information Technology in Construction* (3). The remaining journals' publication rates varied between one to two articles during the considered period.

The bibliometric analysis also reveals that a significant number of publications have been conducted in the USA, with 18 publications, followed by China (12), Australia (9), Hong Kong (8), UK (7), Canada (7), Germany (6) and Italy (6). The remaining countries had less than six articles published during the considered period. Furthermore, the top 10 most cited papers are summarised in Table 7.


**Table 5.** Journals' articles on BIM–IoT integration, for each FM core competence, from Web of Science and Scopus databases.

**Table 6.** Annual publications of journal articles on BIM–IoT integration for FM (\* until February 2021).


**Table 7.** Top 10 of the most cited articles sorted by the number of global citations (until February 2021).



**Table 7.** *Cont.*

Although the most cited article overall is the one written by Wang in 2013, the articles with the highest number of citations per year are those of Tang et al. (2019) with 29.5 Citations Per Year (CPY) and Li et al. (2018) with 28 CPY. Other articles most cited per year are Zhong et al. (2017) (27.5 CPY), Park et al. (2017) (22.8 CPY), Dave Bhargav et al. (2018) (20 CPY). It emerges that if we exclude the first, which is a general review on BIM-IoT integration, the most cited articles mainly concern project management and risk management. In Table 8, each article is assigned to a single core function according to the title and abstract analysis made by authors. Accordingly, an FM core competencies content analysis to generate patterns and trends of existing research has been done.

**Table 8.** Selected articles assignment to a single FM core function according to the title and abstract analysis made by the authors.



**Table 8.** *Cont.*

#### **5. Discussion**

The bibliometric analyses described in this paper identified the main characteristics of the literature in BIM, IoT and the integration of the two aiming to Facilities Management. In this section, an overview of BIM-IoT integration in the several FM core competencies is provided, and areas where further research is required within the scope of each core competence are suggested.

#### *5.1. FM Core Competence: Finance and Business*

The Finance and Business core competence concerns economic aspects, and it deals both with significant financial investment and operational expense. The only article concerning this competence [47] proposes a framework in which blockchain technology, smart sensors, smart contract and BIM are integrated. The proposed framework is meant to guide IT developers to design and implement an automated payment system (based on these new technologies) that aims to solve the security of payment problems. This application of multiple advanced technologies simultaneously and its related workflow are new to the current body of knowledge from both technical and managerial perspectives. In the article, smart sensors, at critical points across the entire supply-chain, provide live location and status information automatically onto a BIM model. Furthermore, smart sensor data are also stored on the blockchain network, providing an alternative system that will allow automated payment of fulfilled contractual obligations, resolving late-payment or non-payment-related issues.

Although this research's findings have undeniable advantages, this study is based on a specific blockchain platform. Further studies could adopt other blockchain platforms more suitable in upholding the security of payment. Furthermore, the research does not consider human tampering to commit fraud during the process. Even the authors suggested that subsequent studies should also consider fraud or any other human interventions that may influence systems operation. Hence, additional security layers and/or network security techniques should be investigated.

Finally, a possible main limitation of this framework hindering its adoption in the construction industry is the need for readily available money. The framework, providing automatic payment upon completion of the work, would jam in the case of lack of funds. If the client were temporarily experiencing a shortage of money in the course of the process, the automated payment would be blocked and the entire process would be interrupted.

Smart contracts and blockchain technology will undoubtedly be two essential elements in the future of FM. Future research should focus on these new technologies and challenges presented by them during the facility's whole life cycle. Several blockchain platforms should be investigated to provide the most suitable and secure solutions to the issues addressed.

#### *5.2. FM Core Competence: Human Factor*

This core competence focuses on protecting the environment and the people who use the facility, minimising risks and liabilities and positively impacting all stakeholders. All the articles belonging to this competence concern indoor environmental monitoring. Four out of seven articles [48,51–53] are on air quality monitoring. Two articles, [49] and [50], are about thermal comfort. Zaballos et al. (2020) [54] discuss environmental monitoring and emotion detection to provide insights into spaces' comfort level. Almost all articles focus their attention on the BIM–Wireless Sensors Network (WSN) connection, aiming to visualise real-time sensors data on the BIM model. The only exception is the research conducted by Zhong et al. (2018) [51], where the BIM model is used to extract building information stored in a tabular format and converted into ontology instances.

Table 9 shows the type of monitored data in each article.

**Table 9.** Human factor core competence: type of sensors used in studies on BIM–Wireless Sensors Network (WSN) integration for indoor environmental monitoring.


The integration between BIM and WSN offers great advantages to the monitoring systems developed in the various research studies. Through this integration, it is possible to better visualise a multitude of data relating to environmental monitoring and associated with multiple elements and spaces. Following this integration and creating the database containing all environmental data (e.g., temperature, humidity, light, noise, etc.), it is possible not only to monitor thermal/air quality problems to ensure comfort for users but also to detect the need for maintenance of building components.

However, during these processes, interoperability between different information systems and information sharing between various stakeholders remains challenging. Management of these heterogeneous data should be further investigated. Moreover, battery capacity and operation duration could be a significant limitation of a WSN. Therefore, it is necessary to consider adopting high-capacity batteries or a fixed power source for long-term operation.

To conclude, protecting the environment in which people live/work is certainly among the priorities that FM will have to face in the near future. Although the use of new technologies and sensors is widespread and certainly not new, the main challenge for this (but also other) core competencies is data/information interoperability. Finally, a novelty that emerged in this review is the improvement of the comfort level in facilities spaces through users emotion detection. In this direction, more effort should be focused to better fit building spaces to users.

#### *5.3. FM Core Competence: Leadership and Strategy*

This core competence focuses on aligning the facility portfolio with the organisation's missions and available resources. According to IFMA [13], sub-competencies in "Leadership and Strategy" include:


There are few studies on this competence as for the previous one. After refining the query, only two studies remained (Table 8). Both articles deal with "decision-making" from two different points of view. Niu et al. (2016) [55] discuss several scenarios about using smart construction objects and their augmented capabilities of sensing, processing, computing, networking and reacting to alleviate human beings' incapability in decisionmaking. The Industry Foundation Classes (IFC) format is adopted to represent these objects in a virtual environment. With their innovative properties, smart construction objects can contribute to data collection and information processing and make autonomous decisions, eliminating human errors in the process and saving time. Although smart construction objects have undeniable advantages, there are still several limitations and challenges to fully exploit their potential, particularly cultural changes, new costs, Artificial Intelligence (AI) acceptance and organisation readiness.

On the other hand, Chang et al. (2018) [56] try to support complex decisions requiring interdisciplinary information using sensor data and the BIM model. Their research also deals with the design of a common platform allowing communication among sensors with different protocols and how visualisation may help make energy-saving management decisions. This visualisation allows us to see different values distribution in different contexts and make appropriate adjustments in each context. In addition, in this core competence, the key point is data/information integration from different sources. In the near future, this is undoubtedly the main problem that researchers will have to face due to the multiple and varied data sources and platforms.

#### *5.4. FM Core Competence: Operations and Maintenance (O&M)*

An important role in FM is to manage operations and maintenance of the facility. To do this, a good knowledge of building systems, structure, interiors and exteriors is required to ensure that systems operate efficiently, reliably, safely and in compliance with standards and regulations.

This core competence is one of the most investigated, and one of the earliest studies was conducted by Rio et al. (2013) [57]. The review showed a high growth rate in this category: ten out of fifteen articles were published in the past two years.

Among the sixteen reviewed articles in this category, nine [57–63,65,70] are about Structural Health Monitoring (SHM). BIM–sensor integration for SHM has been addressed since 2013, and interest in this topic has remained constant over the years. Through datadriven SHM techniques, it is possible to improve information management on structures health, safety and hazard mitigation. However, traditional approaches are insufficient to manage a large amount of data and information to conduct systematic decision-making for future maintenance.

The first attempt to create a connection between BIM and real-time data was made in 2013 by Rio et al. [57]. In their research, sensors data are stored within the BIM model. This strategy, however, could prove counterproductive as too many data from different types of sensors could weigh down the model.

Subsequent studies [58–63,65] propose an information modelling framework for supporting SHM, which includes an external database to facilitate storage, sharing and utilisation of gathered data. Authors, in their studies, propose approaches that support dynamic visualisation (within the BIM model) of some key structural performance parameters and enable continuous updating and long-term data management, generating models compliant with the IFC standard.

Such tools aim to facilitate decision-making on maintenance and risk management, avoiding manual errors resulting from visual inspection of the structures.

Furthermore, in their study, Fitz, Theiler and Smarsly [63] introduce the concept of the Cyber-Physical System (CPS) and present a metamodel for describing it. In their paper, communication-related properties and behaviour of CPS applied for SHM are described. Moreover, system components relevant to communication are specified. Then, the metamodel to formally define a CPS is proposed and mapped into the IFC schema.

On the other hand, the remaining articles that do not deal with SHM address equipment maintenance [66–68] and space management [64,69,71,72]. Here too, collected sensors data are stored in an external database.

The most relevant work in this area is, probably, the research of Cheng et al. [66]. They developed a data-driven predictive maintenance framework based on BIM, IoT and machine learning algorithms. Both Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used to predict Mechanical, Electrical and Plumbing (MEP) components' future conditions with reasonably accurate results. Even if other prediction methods are taken into consideration, the proposed framework has significant implications: (a) fault alarming in an early stage avoiding failures; (b) future condition prediction (knowing in advance the failure timing); (c) minimising or avoiding overtime costs by preparing maintenance materials and tools ahead of time.

Articles that deal with management and maintenance services [64,69] focus primarily on occupancy control even if these systems are not always reliable due to their difficulty in counting people in crowded spaces.

In the O&M context, a first conclusion may be made: reviewed articles suggest that future research should focus on facing challenges presented by managing and visualising data acquired during the whole life cycle of the facility, not only during a single phase. Datarich BIM models will be necessary to support facilities monitoring and applications fully.

Furthermore, many proprietary file formats are used in most articles. To streamline workflows and improve interoperability, it may be appropriate to increase the use of open formats.

Finally, further studies are required to automatically identify critical locations in which sensors are needed, types of sensors required to monitor critical elements and sensors data integration to improve O&M management.

#### *5.5. FM Core Competence: Project Management*

Another essential core skill in FM is Project Management (PM). Projects can vary in scope, complexity, duration and financial risk. According to IFMA, sub-competencies of PM include planning and design, execution and delivery, and evaluation.

Most of the articles concerning the PM [73–79] deal with the topics of real-time tracking of personnel, materials and equipment to enhance the security, safety, quality control, logistics and productivity monitoring. To do this, BIM and Radio Frequency IDentification (RFID) are the most used technologies [73,74,76–78] to implement localisation of people and objects. The proposed systems have a reliable accuracy rate, and RFID localisation systems have great potential in practical applications and could improve resource allocation efficiency and decrease human errors. Instead, Park et al. (2017) [75] developed a tracking system based on the integration of Bluetooth Low Energy (BLE) technology, motion sensors and BIM. This integration aims to achieve more accurate tracking that reduces and compensates for the sensors' errors.

On the other hand, Hamooni et al. (2020) [80] proposed a method that uses BIM interoperability and wireless sensors to monitor concrete maturity and control the concrete formwork process. BIM allows for the calculation of formwork removal time based on the maturity and strength data collected from sensors inside the concrete. This system will allow the concrete placement process to be continuously monitored and controlled and the curing time before formwork removal to be reduced, thus affecting construction management and project controls by (a) reducing the time required to complete the work, (b) avoiding project delays and (c) lowering unnecessary formwork rental expenses.

In conclusion, the main technological challenges found are related to the location and coverage of the sensors network and the signal strength of the router/hotspot. A significant problem that could arise is the stability of networks for communicating information.

Future research may include improvement of these systems and platforms by incorporating more functions related to the PM sub-competencies and productive analysis (e.g., future workforce estimation or a deep investigation of impacts on the total cost and time of a construction project resulting from the use of BIM–WSN integration).

#### *5.6. FM Core Competence: Quality*

Quality is one of the less investigated core competencies. It concerns needs and expectations on the facility and facility's services, aiming to improve facility organisations' and service providers' performance.

Both the articles on this topic [82,83] concern the quality of building components/ construction work to ensure that specifications are implemented according to the project. Digitising information allows detecting design errors or poor performance. Both research studies integrate BIM validation tools to assure BIM quality.

#### *5.7. FM Core Competence: Real Estate and Property Management*

"Real Estate and property management" core competence is about the management of physical assets to enhance users' experience to meet asset owners' strategic objectives and to optimise real estate value. It is one of the least investigated core competencies. Among the reviewed articles, only two of these belongs to real estate and property management competence. Notable is the article of Atazadeh et al. [84], which discusses the use of BIM for defining the legal ownership of IoT-generated data, which are part of the asset value. There are no specific regulations or laws that define the retrieval and use of IoT data considering the appropriate legal rights and responsibilities. Rights, restrictions and responsibilities related to the use of IoT data in multi-owned buildings could be better defined using the BIM environment.

To conclude, as also highlighted by Moretti et al. [85] in their article, future developments in FM aiming at Real Estate and property management should focus attention again on interoperability and openBIM methodology to support dynamic assets management applications. The main issue in this context is the scarce as-built information. Supporting data integration, open formats and interoperability makes it possible to achieve better solutions for building management.

#### *5.8. FM Core Competence: Risk Management*

Risk Management plays a central role in FM and, unsurprisingly, is among the core competencies most investigated by researchers worldwide.

The articles belonging to this core competence address various issues related to risk management, Table 10 groups them by topic. Most of the articles, twelve out of seventeen, are fairly distributed between fire risk issues and safety in the workplace.

All the research agrees that the integration of data between BIM and WSN will provide an invaluable result for future applications in managing users and workers' health and safety. Fire risk studies focus mainly on (a) defining the fire conditions as well as the location and types of relevant fire-extinguishing tools needed; (b) localisation of trapped occupants in a fire emergency scene; and (c) evacuation/rescue paths optimisation. Proposed workflows and algorithms are BIM-centred, where BIM is integrated to provide geometric information and a graphical interface for user interaction.

**Table 10.** Risk management core competence: articles grouped by topic addressed.


Relevant studies in this field have been conducted by Cheng et al. [90] and Chou et al. [95]. Their studies are quite similar and propose a system based on a Bluetooth sensor network that can be used (a) to early detect a fire (b) to plan evacuation/rescue routes and to guide building users in emergencies, (c) for dynamic 3D visualisation of fire events and (d) for bidirectional human-machine interactions to optimise evacuation/rescue efforts. The proposed systems could reduce the number of casualties, support the rescue process and emergency evacuation, and mitigate the panic among people in cases of fire.

Another interesting research study in "fire risk management" was conducted by Cheng et al. [99]. Their study proposes an approach for adaptive path planning against the rapid environmental changes in fires. To detect the number of people in a building space, the network uses real-time videos from Closed-Circuit Television (CCTV) cameras and deep learning algorithms. In addition, an IoT sensor network (detect temperature, carbon dioxide and carbon monoxide) is used to detect hazardous areas. The BIM model provides floor plan information, sensors location and a simplified visualisation model during evacuation. Eventually, research suggests that it is possible to evacuate people through AR devices along the shortest path while avoiding congested and hazardous areas.

Research studies concerning workers' and building users' health and safety have investigated the integration of BIM with several types of wireless sensors to provide a centralised database with updated real-time data throughout the building lifecycle, starting from the construction phase. Currently, safety monitoring practices primarily rely on "manual" observation, which is labour-intensive and error-prone [92]. Therefore, the impact of sensor-based safety management systems, coupled with the BIM environment, could improve health, safety and emergency management.

In conclusion, systems coupling BIM models and sensors can continuously monitor the built environment in an automated way. They can be used for various purposes: (a) to prevent accidents by notifying workers of incoming hazards; (b) to notify safety managers or site supervisors about unidentified or newly appearing threats; (c) to monitor the environmental conditions of a confined space; (d) to better manage emergencies (e.g., fires) by providing optimised escape (or rescue) routes.

However, more research needs to be conducted to make these systems interoperable with existing sensor systems and minimise computational times to avoid any delay in emergency response operations. Furthermore, these systems could automatically detect and document near misses to prevent better accidents and, thus, to improve safety further.

One of the major limitations is the sensors reliability and transmission over long distances, which can cause false alarms. Other important limitations are related to the accuracy of deployed devices, which may be reduced due to water presence, to the presence of fire and/or smoke, which could impact accuracy and signal propagation. Moreover, energy demands may limit continuously monitoring sensors if not wired. Eventually, these BIM-based systems' performances rely on the accuracy of the BIM model. Therefore, having an updated BIM model is essential.

#### *5.9. FM Core Competence: Sustainability*

Sustainability, which is a legal obligation in some countries, could also be considered as a social responsibility that often turns into an economic advantage for asset owners. Facility managers are expected to act in order to protect the environment and the people who use their facilities while supporting organisational effectiveness and minimising risks and liabilities. Subcompetencies of sustainability include energy management, water management, materials and consumables management, waste management and workplace and site management.

Sustainability is among the most investigated FM core competencies. Most of the articles concerning sustainability deal with how buildings' energy demand can be reduced through Information and Communication Technologies (ICT). Furthermore, the ICT application in several processes (e.g., BIM) and scenarios have been investigated. In most cases [52,103,104,106–108,110–117,119] BIM is used to process or display buildings' geometric data, FM data and energy data collected through sensors. Among these articles, References [104,111,113,115–117,119] focus on users' behaviours. These researches aim to raise users' awareness of energy efficiency and consider building users as a primary factor to improve energy efficiency and IEQ. Results put forth the use of real-time monitoring systems and suggest a controlled interaction among users and heating systems to improve energy performances and comfort.

An interesting approach to interact with users has been made by Francisco et al. [116]. They propose a method of combining data and graphic representation through spatial and colour coding techniques in BIM. Through this type of information representation, users can access complex information through a simple interface. In this way, it is possible to improve the interpretation of energy data and increase the user's involvement in the building's management, consequently improving building consumption, comfort and health. Furthermore, the proposed method could be applied to other factors such as water consumption, room temperature or indoor air quality.

Only two studies [52,114] deal with/involve artificial neural networks (ANNs) in sustainability, probably because it is a relatively new topic in the AECO sector. The study of McGlinn et al. is relevant. [114]. They propose an intelligent monitoring and control interface for efficient energy management using BIM and Semantic Web to integrate smart buildings, sensors and software components like artificial neural network (ANN) and genetic algorithms (GA). This interface provides suggestions based on the building's sensor measurements and proposes these suggestions to the Facility Manager. However, there are still issues related to interface usability for non-technical users.

The other two studies dealing with sustainability discuss: (a) a framework integrating the information necessary for green buildings design and their automated evaluation process [105] and (b) a BIM–RFID-based approach with the potential to improve resource reuse and efficiency [109]. Although through the two approaches presented in the articles mentioned above, it is possible to gain advantages in the design processes of Green Buildings, it is evident that research in these fields is still at an early stage.

When discussing sustainable approaches aimed at controlling energy consumption in a building, it is impossible not to mention the Building Energy Management Systems (BEMSs). A BEMS can fully monitor and control the building's energy needs through building energy data collection, performance analysis and equipment control [139]. Through better energy management in a building, a BEM not only reduces energy consumption and costs but also improves occupants' comfort [139]. Conceptually, a BEMS architecture has different layers: a sensor/actuation layer, a computational layer, an application layer and, in some cases, also a user interaction layer [114].

Hence, on the one hand, the BIM model provides a series of static data relating to the building (not only geometric and spatial data but also other information according to the BIM's several dimensions). On the other hand, the BEM system takes the role of collecting data from sensors in the building on-site. The synergy between these two environments (BIM–BEM) can positively affect building energy management, especially

by users' involvement. Among selected articles, the first attempt at BIM-BEM integration dates back to 2013 with the research carried out by Osello et al. [104]. They developed an ICT infrastructure made of heterogeneous monitoring and actuation devices to reduce energy consumption. Finally, they used BIM and interoperability to process and visualise all data essential for energy simulations and for FM. Other studies, from Lee et al. [112] and McGlinn et al. [114], show that BIM is a useful approach for the visual representation, management and exchange of information on all aspects of a building. In particular, in Lee's research, BIM was used to develop an energy management platform. In their study, BIM was used to visualise gathered environmental and energy consumption data. In this way, facility managers could better manage buildings energy consumption and control buildings' equipment.

Another significant attempt at BIM–BEM integration has been made by Kang (2020) [118]. Kang, in his research, proposes a BIM-based Human Machine Interface (HMI) framework for space-based energy management. The proposed framework links data between BIM and BEMS, which are heterogeneous systems, aiming at space-based real-time energy monitoring. Furthermore, as it is challenging to use a BIM data structure if it does not fit into the energy management system, this researcher also defines requirements for developing a BIM database suitable for the proposed framework.

In conclusion, although there is much to be done in built environment sustainability challenges, four major steps should be accomplished: (a) fine-tuning of the interaction between environmental sensors data and Artificial Intelligence (AI) or optimisation algorithms; (b) developing sustainable and innovative user interaction strategies; (c) focusing attention on other sustainability sub-competencies (e.g., water management, materials and consumables management, waste management); (d) aiming at BIM–BEM integration, overcoming problems due to the differentiation of communication protocols.

#### *5.10. FM Core Competence: Information and Technology Management*

Although the whole topic of BIM–IoT integration could be discussed in this section, only articles concerning sub-competencies such as technology needs assessment and implementation, maintenance and upgrade of technology systems or protection and cybersecurity are addressed here. Some of the analysed articles may be associated with other FM competencies, but they are discussed in this FM core competence if:


Many of the reviewed articles suggest no generic approach to assist in creating software services and applications combining sensor data with BIM models. Articles address the engineering complexity associated with integrating sensor data with BIM to facilitate real-time operational performance information management and permit proactive operational and maintenance decisions in many ways. Only three articles [123,127,131] discuss the development of collaborative BIM-based AR/MR/VR approaches. Interesting research has been carried out by El Ammari et al. [127], who developed a Mixed-Reality framework for facilities management with two modules: a field AR module and an office Immersive Augmented Virtuality (IAV) module. These modules can be used independently or combined using interactive visual collaboration, with an improvement in field task efficiency.

Another noteworthy research study was carried out by Kazado et al. (2019) [128], who presented three approaches for BIM-sensors integration to enable visualisation and analysis of real-time and historical data. Despite being probably the first work using Autodesk Navisworks software to implement a user-friendly interface that integrates the existing building sensor technology and BIM process, the use of a closed data format (Autodesk's files format) instead of an open one could be a limitation.

Most of the studies highlight the risk of losing competitiveness both on the local and international markets if stakeholders in the construction sector slow down the adoption of new technology. The construction industry appears to be already outdated when compared to other industrial sectors. Nevertheless, according to the articles, stakeholders seem reluctant to invest, especially in costly innovative technological devices. Therefore, many researchers aim to reduce the initial investment costs while proposing innovative solutions that bring added value to the FM processes.

#### **6. Conclusions**

Although still in its infancy, the construction sector's digitisation process is underway, aiming to create an ever-larger network of cyber-physical connections, supported by the abundance of sensorized and networked elements. The analysis of data generated by sensorized building components and systems will allow using connected digital models to improve future design and increase the environmental, safety and financial performance of the digitally built environment.

This document provides an overview of BIM and IoT integration in FM. From a query on Scopus and Web of Science with more than 900 results, 99 articles were identified and reviewed as the most relevant references. Existing gaps and future research directions were outlined.

BIM now supports many technological advances that the industry is witnessing, albeit with some limitations. Although BIM is widely used in the building design phase, there is still much to do for its use in Facility Management. Nevertheless, BIM can be considered a natural interface for IoT/real-time data implementation. Several researchers have begun to explore the potential synergy between these two environments.

From the literature review, it emerges that the BIM and IoT integration research is in an early phase. Most research works are still in the conceptual stage, even though some studies are quite thorough and propose solutions tested in real-world applications. The main obstacles preventing the uptake of these new technologies include (1) in most cases, the lack of a BIM approach that meets the information requirements and fully exploits the potential of the digital model; (2) the fragmented nature of the AEC sector; and (3) shortage of real-life use cases demonstrating potential benefits.

General remarks found on BIM for FM are related to the need to


Furthermore, one of the main challenges in BIM–IoT integration is coupling dynamic real-time data to the model database. In this context, future studies are needed to


BIM–IoT data integration has a new added value in the market: the physical object is a product that carries information throughout its life cycle. This will significantly help the construction industry, which, mimicking more industrialised sectors, has just begun its journey from being product-oriented to service-oriented. However, to take advantage of this transformation, the integration of data into BIM models needs to be managed in the best possible way.

BIM and IoT studies are often based on proprietary files and closed ecosystems, where information is not yet shared openly among stakeholders. Hence, subsequent studies within the BIM-IoT integration domain should focus their attention on open data and open communication standards.

On the other hand, WSN could be considered the IoT solution for monitoring and recording the physical condition of buildings and environmental monitoring management. Research hass proved that both high costs and ineffectiveness of WSN devices can be

avoided if information requirements (data types, data frequencies, WSN devices' location, etc.) are appropriately set at the very beginning of the asset lifecycle.

The major problems encountered in the use of WSN concern:


In conclusion, this review highlights four key areas to be further studied:


A deep review of 99 articles related to the eleven IFMA FM core competencies highlights four main knowledge gaps in the emerging sector of BIM and IoT integration for FM. These are related to the back-propagation of information from the use stage to the design one, to new technologies exploitation and final users' involvement in improving buildings sustainability. This may help further research advancement for studies to improve built environment management.

**Author Contributions:** Conceptualisation, A.M., M.C.D. and F.R.C.; methodology, A.M., M.C.D. and F.R.C.; software, A.M.; data curation, A.M.; writing—original draft preparation, A.M.; writing review and editing, A.M., M.C.D. and F.R.C.; supervision, M.C.D. and F.R.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Marina Bonomolo 1, \*, Simone Di Lisi <sup>2</sup> and Giuliana Leone 1**


**\*** Correspondence: marina.bonomolo@unipa.it

**Abstract:** Over the years, building information modelling (BIM) has undergone a significant increase, both in terms of functions and use. This tool can almost completely manage the entire process of design, construction, and management of a building internally. However, it is not able to fully integrate the functions and especially the information needed to conduct a complex energy analysis. Indeed, even if the energy analysis has been integrated into the BIM environment, it still fails to make the most of all the potential offered by building information modelling. The main goals of this study are the analysis of the interaction between BIM and energy simulation, through a review of the main existing commercial tools (available and user-friendly), and the identification and the application of a methodology in a BIM environment by using Graphisoft's BIM software Archicad and the plug-in for dynamic energy simulation EcoDesigner STAR. The application on a case study gave the possibility to explore the advantages and the limits of these commercial tools and, consequently, to provide some possible improvements. The results of the analysis, satisfactory from a quantitative and qualitative point of view, validated the methodology proposed in this study and highlighted some limitations of the tools used, in particular for the aspects concerning the personalization of heating systems.

**Keywords:** BIM; BEM; simulation modelling; dynamic simulation

#### **1. Introduction**

The large-scale diffusion of building information modelling (BIM) tools for architecture has led to an enormous evolution of these digital means [1]. Today, it includes multiple functions capable of carrying out numerous analyses (e.g., structural, energy, metricestimative, etc.) on a single virtual model of the building. In particular, some aspects, e.g., energy ones can be implemented by using a monitoring system connected to the BIM. Jen-tu and Vernatha [2] proposed an application of Building Information Modelling in establishing the 'BIM based Energy Management Support System' (BIM-EMSS) to assist individual departments within universities in their energy management tasks. They installed sensors for occupants and other equipment such as electricity sub-meters that constantly logging consumption, and developing BIM models of all rooms within individual departments' facilities, data warehouse, building energy management system that provides energy managers with various energy management functions, and energy simulation tools (such as eQuest). In addition, Alahmad et al. [3] integrated BIM with a Real Time Power Monitoring (RTPM) System, and Jeong-Han Woo et al. [4] presented a prototype of BIM-based Baseline Building Model (B3M) for ageing commercial buildings. When the aim is not the realtime monitoring and there is not an existing building or the possibility to install sensors and other devices, it is necessary to use energy simulation tools and connect them with the BIM. Although BIM software is technologically advanced and able to best meet the needs of professionals in various applications, the energy simulation function needs many improvements. Some researches started to investigate the topic of the "green dimension" [5]. Indeed, to optimize the design in terms of energy efficiency, it could be useful that the energy simulation phase be carried out in meantime with the development of the project [6]

**Citation:** Bonomolo, M.; Di Lisi, S.; Leone, G. Building Information Modelling and Energy Simulation for Architecture Design. *Appl. Sci.* **2021**, *11*, 2252. https://doi.org/10.3390/ app11052252

Academic Editors: Jürgen Reichardt and Igal Shohet

Received: 27 January 2021 Accepted: 1 March 2021 Published: 4 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and to make choices based on intermediate calculations, and therefore constantly modify the project until the objectives are achieved [7]. To do this, it is often necessary to build another digital model, called BEM (Building Energy Model), or implement the BIM model with all the information needed for energy simulation (occupation of the rooms, calculation of thermal bridges, analysis based on hourly climate data, etc.) [8]. The main issue is the lack of bidirectional interaction between the two models. Furthermore, one of the main issues is data transmission. For this reason, BIM for energy simulation is often used only for early design step [9]. The aim should be to work with a unique model [10]. In last decade, many researchers presented studies on BIM's application in energy analysis and simulation [11] and proposed solutions for the interoperability between BIM and energy simulation tools [12]. Some of them developed tools to integrate BIMs. Bratch et al. [13] evaluated the possibility of integrating a thermal load prediction metamodel to building information models to facilitate the data exchange process. To do this, they developed a tool to validate the viability of this integration using gbXML, and it was submitted to validation tests. Ramaji et al. developed an extension for OpenStudio able to transform building information models represented in Industry Foundation Classes (IFC) files into building energy analysis models in the OpenStudio data format. Kamel et al. [14] developed and presented a new tool called ABEMAT (Automated Building Energy Modelling and Assessment Tool), able to make the building energy simulation using BIM automatic and to give fine-grained outputs. It receives a gbXML file and provides users with the amount of heat transfer through each building envelope element.

Kim et al. [15] developed and validated a library for BIM for the building energy simulation (ModelicaBIM library) using an Object-Oriented Physical Modeling (OOPM) in the scope of the building envelope.

A design-decision-supporting tool for the conceptual phases of design and throughout the design process based on a BIM template has been designed by El Sayary and Omar [16]. In particular, the aim was to develop a simple tool to calculate how many photovoltaic solar panels can be installed to reach a zero-energy building by substituting all electric devices. Xu et al. [17] investigated the application of BIM for addressing the building energy performance gap. The authors provided a clear set of guidelines for how BIM could be used, by each function, to overcome the BEPG to reduce global emissions driven from the building and construction sector. These cited studies provided good outcomes and results. Nevertheless, a goal of this study is to test a commercial existing tool, easy to be found and to be used. Other authors applied existing commercial tools.

Additionally, Utkucun and Sözer [18] proposed a method to determine the interoperability of the utilized programs for evaluating a building's energy performance and indoor comfort through the BIM approach. To do this, they built three main analysis models: An architecture of the building (with the 3D building model), indoor comfort conditions (with the computational fluid dynamics for natural ventilation simulation), and energy performance (with a building energy model specified by the building architecture and its systems). Then, they integrated them through a BIM platform. In order to investigate the potential and limitations of applying BIM to energy management and simulation in the operation lifecycle phase of a service building, Rodrigues et al. [19] developed a service building BIM model in Autodesk Revit and used Energy Analysis for Autodesk Revit that automatically generated the Building Energy Model (BEM) from the BIM model and performed a cloud-based simulation in Autodesk Green Building Studio (GBS). They found input limitations of GBS, mainly in HVAC systems customization, compromise the representation and energy performance evaluation of the building under actual operating conditions. For this reason, they affirmed that GBS is more adequate for early buildings' lifecycle stages where energy simulation results may support decisions that aim to improve the buildings' energy performance during the operation phase.

Tushar et al. [20] used the software Autodesk Revit together with the energy rating tool (FirstRate5), and BIM-enabled life cycle assessment (LCA-Tally) to quantify, compare, and improve the building design options to reduce carbon footprint and energy

consumptions in residential dwellings. Alam and Ham [21] compared FirstRate5 with Archicad EcoDesigner developing three building types. They found significant differences between simulations, being, measured areas, thermal loads, and potentially serious shortcomings within FirstRate5, that were discussed along with the future potential of a fully BIM-integrated model for energy rating certification in Victoria. In Farzad Jalaei and Ahmad Jrade's study [22], an integrated method that links BIM, energy analysis, and cost estimating tools with a green building certification system was presented. The aim was to calculate the potential gain or loss of energy for the building and to evaluate its sustainability based on the US and/or Canadian Green Building Council.

Right now, the data transmission between BIM and BEM is possible through two types of file format: IFC (Industry Foundation Classes) and gbXML (Green Building Extensible Markup Language). Both have important advantages. The IFC format is the standard format for information exchange in BIM modelling and is the only format to have a certification. It is possible to load most of the information concerning a building except for the energy analysis data, such as occupancy profiles, data relating to external and internal temperatures, systems, etc. The gbXML is a format based on IFC, but containing all the information of an energetic nature. It was developed to operate in this field, and it is the most popular among energy analysis software. Indeed, some software have been imported only in this format to the detriment of the IFC (IES-VE [23], EnergyPlus [24], eQUEST [25]). However, in a review paper, Gao et al. [26] investigated the data transfer between BIM to building energy modelling and they found that the development of BIM-based building energy modelling is still at the initial stage and few methods can be guaranteed to generate reliable building energy models from building information models without errors.

In this light, it has to be remembered that the choice of the tool is very important because, based on the software used, working times can increase or be reduced. On the other hand, it is possible that once the BIM model of a building is completed, it is exported to software for energy analysis, and this does not interpret the geometries well or even fails to import a lot of information previously entered. It makes the work useless. In this case, the technician has to manually enter each missing item or in the worst case, he/she has to build a model specifically for the energy analysis. This procedure can be more or less long depending on the building under consideration. It certainly differs from the BIM aim that provides a faster and interoperable workflow by means of a single model.

The most common choice in the architectural-energy field is to proceed with the architectural design and postpone the energy analysis at the end. It provides a separate and specific calculation, and this process often leads to a meaningless analysis as this is ascertained.

The process to be adopted must be the opposite: Support the design with energy analysis from the early stages (which by its nature will be a summary, as you will not yet have all the parameters to be able to make a more complex analysis) and direct the project towards a more eco-sustainable way. In this light, obviously, a real transition, from BIM to BEM is required. It has to be managed by single software that is based on the digital model that is being built, and which automatically updates the BEM on this basis, allowing for a reduction in work-time and a more energy-saving design.

In this study, a methodology based on the full interaction between Graphisoft's BIM software Archicad and the plug-in for dynamic energy simulation EcoDesigner STAR was tested. They have been selected after a study of the rules on energy analysis, an examination of the operational potential of different software on the market, and a research conducted by a wide scientific community interested in various capacities in issues related to the interaction between architecture and energy analysis. In particular, the choice has been based on some main criteria such as versatility, i.e., the presence of integrated functions that allow BIM and BEM modelling, compliance with standards (the software or plug-in for energy analysis must meet the requirements set by the most advanced energy diagnosis standards, such as UNI EN ISO 52016-1 for the calculation in dynamic hourly regime, ASHRAE 140-2017 and UNI/TS 11300 on the monthly average stationary calculation and

certification required by buildingSMART for IFC certification; and versatility thanks to the presence of integrated functions that allow BIM modelling and BEM modelling.

The main goals are the analysis of the interaction between BIM and energy simulation and the identification of a methodology that allows overcoming the above-mentioned limits. With this purpose, Graphisoft's BIM software Archicad and the plug-in for dynamic energy simulation, EcoDesigner STAR, were used. They have been selected after a careful study of the rules on energy analysis, an examination of the operational potential of different software on the market, and research conducted by a wide scientific community interested in various capacities in issues related to the interaction between architecture and energy analysis. Thanks to the state-of-art analysis, the advantages and the disadvantages of the existing tools were highlighted and compared. The application of the selected tool on an existing case study gave the possibility to further study and to test the methodology. Finally, the analysis of the lacks suggested some improvements that can be done. '

#### **2. Methodology of the Study**

This study *starts* from the idea that BEM can be useful for energy analysis building, but there is some issue due to the lack of bidirectional interaction between BIM and BEM, as *observed* in literature.

This paper aims to study a method to carry out energy analysis using these tools. To do this, it was *hypothesized* to test a combination of two tools (selected after an investigation of some existing tools doing *background research*). They were used to perform the *experiment* (test) that provides information and data. These latter were analyzed and interpreted to formulate the *results* by including advantages and disadvantages to performing an energy analysis by using the selected tools. In *conclusion*, the study shows that it is possible to perform energy analysis through the use of BIM, but it needs some improvements.

The research was conducted in several steps. In the first phase, some of the most common new design tools and the most recent standards for energy analysis were studied. From this first analysis, it was possible to list the disadvantages and the advantages. Once a tool was selected that matched the required characteristics, it was validated using a simple building. Then, an existing building was chosen as a case study to apply the energy analysis methodology in a BIM environment. It was defined through the application of a test model. Figure 1 shows the workflow diagram.

**Figure 1.** Workflow diagram with research framework.

The studies conducted in the first phase highlighted one of the main problems that hindered the diffusion and use of energy analysis plug-ins in the BIM environment, namely the lack of integration between the parametric architectural model with information content (BIM, Building Information Model) and the energy model (BEM, Building Energy Model). The problem is the possible loss of information during the conversion from BIM to BEM, or worse, the lack of interpretation of the entire model. It is clear that this problem diverges the actual workflow from the characteristic one of BIM, based on the principle of

interoperability, i.e., the possibility that allows different professionals to work and exchange information on a single digital model.

As said, the lack of integration between BIM and BEM means that, during the energy analysis phase, it is necessary to build a new model or implement the Building Information Model with new information not managed by it.

Once the objective of the study was defined, we moved on to research and the choice of software suitable for supporting a working methodology that allows full integration between BIM and BEM already in the design phase. Various digital tools for energy analysis were evaluated, and the choice of the software used in this study was determined by the evaluation of some of its specific characteristics, such as the validity of the calculations (with reference to current legislation) and certifications in the BIM field. To verify the validity of the calculations, a sample building with characteristics suitable for evaluating the effectiveness of the software was simulated.

The last phase of the study was dedicated to applying the digital tool and the methodology defined for the sample model to an existing building. The choice was determined by the value and also by the simplicity of the building, which allows you to control the result of the simulation process more precisely. The simplicity to which reference is made does not concern the spatial and architectural quality of the building or housing, but rather the relationships with the ground and the characteristics of use that allow a more effective control of the results of the BEM energy analysis. The chosen building, in fact, has simple thermal zones, with no particularities (of use or construction) that could alter the calculation of the energy simulation. Finally, based on the detection of the limits and the lacks, some further possible improvements of the tool have been suggested.

#### **3. From BIM to BEM: Advantages and Limitations**

The study and the implementation of methods to transform a "BIM" to a "BEM" are a topic always more common in the scientific community. Indeed, BEM has a large number of applications in the most varied cases of energy analysis. Moreover, as said, the available tools have limitations (sometimes highly restrictive). For this reason, technicians are not encouraged to use them. The main identified problem is the transmission of data between the BIM modelling tools and the energy simulation tools. It limits the possibility to operate with the least possible number of digital models.

The main formats are:


It is therefore understood that, based on the software used, working times can increase or be reduced to a minimum. It is possible that, once the BIM model of a building is completed, it is exported to software for energy analysis. Sometimes, it happens that it does not interpret the geometries well or even fails to import a lot of information previously entered, effectively making the work already done useless. In this case, the technician has to manually enter each missing item or, in the worst case, he/she has to build a model specifically for the energy analysis. This procedure can be more or less long depending on the building under consideration. This certainly differs from the BIM methodology aim. Indeed, it provides for a faster and interoperable workflow by means of a single model. The most commonly used choice in the architectural-energy field is to proceed with the architectural design and postpone the energy analysis at the end with a separate and specific calculation. Often this process leads to a meaningless analysis as this is

ascertained, which was designed with no room for improvement [26]. Obviously, the process to be adopted should be the opposite: Support the design with energy analysis from the early stages (which by its nature will be a summary, as you will not yet have all the parameters to be able to make a more complex analysis) and direct the project towards a more eco-sustainable way. With this in mind, obviously, a real transition from BIM to BEM is required, instantaneous and managed by a single program. It is based on the digital model that is being built. Consequently, the BEM should be automatically updated. This procedure should reduce the time, the energy spent to work, and should allow a more energy-saving design.

#### **4. Short Review on Existing Tools**

The quality of the product, the possibilities offered, and the use the user must do influence the diffusion of a certain program. The most famous and common software for BIM are Revit (Autodesk, San Rafael, United States) [27] and ArchiCAD (Graphisoft, Budapest, Ungheria) [28] and then, Allplan (Nemetschek, Munich, Germany) [29], and Edificius [30] (Acca Software, Avellino, Italy) [31]. The tools included in these software are quite similar. They are equivalent to many functions and just a few advantages are different. In all cases, as regards energy diagnosis, the use of one of these tools often is not suitable.

The choices on the market are different, each with its peculiarities, therefore the main characteristics of each software are examined below, giving precedence to the most common BIM software and related plug-ins and then to independent programs.

#### *4.1. Revit*

Revit [27] is one of the most popular BIM software, both for its performance and for its compatibility with other programs widely used in the construction sector (also produced by Autodesk). It must be specified in this regard that Revit has not developed great connectivity with software that is not part of the Autodesk suite. It is possible to find compatibility problems even if almost all manufacturers try to interface as much as possible with Revit. Energy diagnosis is allowed through an additional module, Energy Analysis, which integrates the design features of Revit with the analysis features of Autodesk Green Building Studio, an independent cloud service for energy diagnosis based on the DOE-2.2 simulation engine [25] (which complies with the ASHRAE 140-2007 [32] standard).

#### *4.2. ArchiCAD*

ArchiCAD [28], developed by the US company Graphisoft, is one of the two most popular software for BIM design and has the IFC certification of buildingSMART. The energy diagnosis can be carried out both by functions integrated into the program and by a plug-in: EcoDesigner STAR. This latter is an integral part of the program itself. Its calculation engine (VIP-Core by StruSoft) operates in compliance with the ASHRAE 140- 2007 and ASHRAE 90.1-2007 (LEED Energy) standards. Therefore, it operates in a dynamic regime. The main novelty of the plug-in is the integration of the missing tools in the package of standard tools (such as the calculation of thermal bridges or renewable energy) and the ability to export files in .gbXML and .PHPP format, for easier collaboration between professionals and technicians.

#### *4.3. Allplan*

Allplan [29], developed by Nemetschek, is the leading BIM-based software used in Germany. It is among the programs certified by buildingSMART. Regarding the energy functions, it does not have sufficient tools to conduct a correct simulation. So, in 2009, it was implemented with a new module: AX-Energy. This module integrates the software tools allowing it to carry out energy analyses according to Decrees 311/2006 [33] and 115/2008 [34] and UNI/TS 11300-1 [35] and 2 [36] standards, thus relying on an almost stationary, rather than the dynamic, regime.

#### *4.4. Edificius*

Edificius [31] is a software produced by ACCA Software. It is the only Italian program to have received the buildingSMART IFC certification. The construction of a BIM model is accompanied, through an external program by the same company, by the construction of a BEM model for the energy analysis of the architectural building. TerMus used the EnergyPlus energy simulation engine based on the ASHRAE 140-2007 standards allowing analysis in a dynamic regime. Unluckily, it is necessary to install a series of modules, each with its own specific function (for example TerMus-PT calculates thermal bridges and mold risk, TerMus-DIM deals with energy diagnosis and improvement interventions, TerMus-PLUS for dynamic calculation and so on).

#### *4.5. Design Builder*

Design Builder [37] is an independent program based on the EnergyPlus simulation engine (it is its graphic interface), capable of analyzing a building under dynamic conditions from the energy point of view. The software has 3D modelling tools, but it is still possible to import into it a model built with an external program compressed in .gbXML format. Since it is not a software used purely for parametric modelling, it does not have the buildingSMART IFC certification.

#### *4.6. Open Studio*

Open Studio [38] is another graphical interface of the EnergyPlus simulation engine. It is available as a plug-in for the 3D modeling program SketchUp, with the particularity of being free. Being a SketchUp plug-in, its modelling is more intuitive than other programs with the same function (the IFC certificate is missing), while the analysis tools are not as intuitive as those of other software.

#### *4.7. Simergy*

Simergy [39] was developed as an independent program. It uses the EnergyPlus simulation engine, thus operating at a dynamic speed. Its user-friendly graphic interface is particularly effective for its use as a calculation tool, while the 3D modelling integrated in it is not easy and immediate to use. A peculiarity of the software is the possibility of comparing different project hypotheses with relative analyses. Additionally, in this case, since it is not a tool for parametric modelling, the IFC certification is absent.

#### *4.8. TermoLOG*

TermoLOG [40], by Logical Soft, is independent software that integrates parametric modelling tools and energy diagnosis. There is the possibility to import models in IFC format built with other programs. As a parametric modelling tool, it does not have buildingSMART certification. According to the standards dictated by UNI EN ISO 52016 [41], and validated by the Politecnico di Milano according to ASHRAE 140-2017 [32], it operates with a dynamic hourly engine (CENED + 2.0).

#### *4.9. EcoDesigner Star*

EcoDesigner Star [42] is a plug-in integrated into the ArchiCAD software. It is a graphical interface of the VIP-Core calculation engine optimized to work in harmony with the design tool in a BIM environment. This ArchiCAD extension was created with the aim of facilitating the design of buildings, directing them immediately towards more sustainable solutions. It is therefore a design tool and it does not allow the certification of buildings according to Italian standards. So, in this case, certification must be carried out using analysis software mainly dedicated to it. However, it is specified that the software calculations are not to be considered incorrect or non-compliant with current standards. In fact, they are based on data and specific parameters relating to the ASHRAE standard, and in the input phase, these parameters can be modified in order to obtain results in line with current legislation. It is not possible to carry out immediately and automatically to check required by the regulations.

The novelty proposed with EcoDesigner STAR is to have, within a software in a BIM environment, a powerful energy analysis tool. It is possible to work on a single Building Information Model and transform it almost instantly into a Building Energy Model ready to be analyzed. Moreover, it is also possible to orient the design of a building towards more sustainable ways both from the point of view of energy consumption and that of energy production through renewable sources. From the early stages of the project, an energy analysis can be obtained by defining the parameters necessary for the calculation. Therefore, an overview of the performance of the building and guide the designer towards the best-integrated design solution can be carried out. Using this tool, the path to be explored is established, and after having decided all the details of the building envelope and systems, it is possible to proceed with a further analysis, this time more detailed, to know the behavior of the building through a calculation dynamic, on an hourly basis. The integrated plug-in has many advantages, e.g., the possibility to manage the workflow in an optimal manner, and guarantees, both in terms of parameter input and in the output phase, high versatility and a high degree of data customization. EcoDesigner STAR, through the tools already present in ArchiCAD and connected to it, in the input phase allows you to:


In the output phase, it is possible to obtain:


#### *4.10. Selection Criteria and Choice of Software*

The criterion that led to the choice of the specific software is based on the following characteristics:


The following table shows the BIM and BEM software and plug-ins already mentioned in the previous paragraph. For each of them, the greater or lesser compliance with the criteria described above is reported.

Table 1 shows the comparison of the eight examined software.

The first important difference concerns the IFC certifications. Indeed, it can be seen that only the BIM software combined with the plug-in has the third requirement (e.g., the buildingSMART certification).

The stand-alone BEM software, even if equipped with tools for the construction of a BIM model, cannot match the software dedicated to BIM in terms of functions, interoperability, and complexity. The adoption of the BIM Software + plug-in combination can guarantee a faster workflow. It is free from possible simplifications or misinterpretations of the data, resulting from exporting to external software from a different software house. Moreover, regarding the compliance of the software with the parameter relating to compliance with current legislation, many of them do not operate according to the Italian guidelines for stationary and dynamic calculation. Nevertheless, the 8 software examined comply (except for Allplan) with the ASHRAE 140-2007 standards relating to the validity of the calculation adopted. This does not mean that the calculation tools are wrong, but that these programs can only be used for energy diagnosis. For the compilation of energy performance certificates (APE) and other certification documents, different software has to be used. From the examination of these two parameters, the choice can be restricted to a more limited number of software. The BIM + plug-in software solutions that meet the requirements include:



**Table 1.** Comparison of eight examined tools.

 Among the stand-alone plug-in, TermoLOG appears to be the more complete than the competitors Design Builder, Open Studio, and Simergy, based on the EnergyPlus calculation engine, and therefore quite similar. Therefore, the main parameter to choose it is the versatility, or the presence of all functions in a single work environment, in order to limit the use of other software.

 The versatility suggested adopting an integrated BIM + BEM plug-in solution. It offers a much larger package of features and greater interoperability than the stand-alone solution offered by TermoLOG. Furthermore, Revit + Energy Analysis package is not the best choice according to its versatility. Indeed, it needs to be accompanied by other plug-ins (such as Insight for solar analysis) to work. Finally, for these reasons, the ArchiCAD + Ecodesigner STAR combination has been selected. The parametric modelling of ArchiCAD has been implemented and updated. The model can guarantee full compatibility with the EcoDesigner Star plug-in, making the transition from the BIM model to the BEM model almost instantaneous. The adopted solution solves one of the most common problems in the integration between energy and BIM. Indeed, if the BIM model is imported and interpreted without errors or excessive simplifications, the BEM can be built and obtained by simply enriching the information present in BIM from the Energy Analysis Program. If the import/verification step does not take place correctly, it will be necessary to perform a specific BEM modelling. It causes longer time of work and more effort by the designer. Thus, it nullifies the advantages of the BIM workflow. If the model was correctly set, the ArchiCAD + Ecodesigner STAR solution automatically performs the transition from the BIM model to the BEM model. It has the great advantage of not losing any information present in the BIM and recording in real time in the BEM model all the changes made to the BIM model. It enhances the aim of BIM design.

> These considerations can be summarized in graphs in Figure 2.

**Figure 2.** Comparison of the analyzed tools.

" " " " " " Each figure represents a tool. The vertexes are four main characteristics: Versatility, compliance with the standards, certification, and the "workflow-continuity". This latter identified the advantage of not having to open another software to conduct energy analysis. It leads to less interoperability since the changes made to the BIM will not be directly reported in BEM. The value related to the "versatility" was associated according to the number of features available (e.g., Open Studio has 3/5 features, so the value of its versatility is 0.6). The value related to the "compliance with the standard" was calculated according to the number of standards complied with, e.g., Allplan + AX-Energia complies with 1/3 standards, so the value is 0.33. The value of the certification is equal to 1, if the tool is certified, and 0 if it is not certified. The value of the workflow-continuity is equal to 1, if the tool has this characteristic, and 0 if it does not.

#### **5. Modelling and Pre-Analysis of a Simplified Building**

As a first step, the energy analysis of an apartment in a three-story building was carried out to study the characteristics of the software using EcoDesigner STAR, a plug-in for ArchiCAD. It allowed highlighting the main calculation characteristics and detecting the first advantages and disadvantages of using this tool.

The choice was determined by the value and also by the simplicity of the building, which allows us to control the result of the simulation process more precisely.

The structure is made up of a reinforced concrete frame made of rectangular section beams and pillars (30 × 60 cm). The indoor walls are composed of non-insulated brick blocks. It was geo-located in the city of Palermo. As regards the immediate surroundings, it was decided to consider it not bordering other buildings.

The construction of the BIM model was carried out using the construction components and materials already present in the program library. In this way, possible conflict situations were avoided to better control the process. Figure 3 shows the three-dimensional view of the building with the apartment examined in evidence.

**Figure 3.** Three-dimensional view of the building with the apartment examined in evidence.

#### *5.1. Switching to the Building Energy Model*

climate data on those provided by "Reanalysis NCEP" available on the website of the "NOAA Cires Climate Diagnosis Center". The information obtained was compared with " — " Once the BIM model was obtained, the missing information was implemented for the construction of the BEM model. In particular, data related to the project site and its location with related climatic data (air temperature, relative humidity, solar radiation, and analysis of wind speed and direction) were included. The software can download automatically the information from the Strusoft Climate Server. The Strusoft server bases its climate data on those provided by "Reanalysis NCEP" available on the website of the "NOAA-Cires Climate Diagnosis Center". The information obtained was compared with the climatic data used by the EnergyPlus calculation engine. It is based on the data collection commonly known as "IGDG—Climatic data G. De Giorgio" [43] (Figure 4).


**Figure 4.** On the left the climatic data obtained from the Strusoft server, on the right the climatic data G. De Giorgio.

 — —

The comparison shows that the climatic data used by the EcoDesigner STAR calculation engine are in compliance with those used by the EnergyPlus calculation engine, with maximum and minimum temperatures very close to each other: —

	- maximum temperature: 34.0 ◦C—minimum temperature 4.79 ◦C (EnergyPlus).

It was supposed that the slight deviations between the monthly temperatures were due to the different time intervals relied on for data collection. In particular, the EnergyPlus data refer to a period ranging from 1951 to 1970; while those of the Strusoft servers are updated from 1948 to today. Then, the areas of the building characterized by the same orientation, by the same usage profile and above all by the same system (thermal blocks) were defined (Figure 5). It was possible to identify only two thermal blocks (Figure 4): that of the heated rooms and that of the unheated rooms.

**Figure 5.** Screen of the thermal blocks window with 3D visualization of the selected zones.

For each block, an operating profile and a plant system (heating, air conditioning, and ventilation) were set. The software includes different operating profiles (residential, commercial activity, hotel, cinema, museum, and others), but it was chosen to have a more complete overview of the program and its potential, to create a new one. As for the most common software, it is possible to select different schedules for different seasons: One for the summer season (for the cooling plant) and one for the winter season (for the heating plant).

It is possible to assign several thermal blocks to a single system or to assign a specific one to a single thermal block. In particular, it is possible to set the use of a boiler for heating and the production of domestic hot water to several thermal blocks; while the cooling system is, if made with single units serving only one room at a time, it must be assigned for each air-conditioned block. In this case, there is only a natural gas boiler for heating and the production of domestic hot water. Furthermore, there is no type of summer air conditioning and the ventilation is natural as it is normally the case in common homes.

The data relating to the autonomous heating system are very simplified. It is possible to select the nominal power of the element, the type of control (with internal/external sensor or by manual ignition), the type of energy source used, the cost of the energy used, in order to obtain an estimate of the costs and consumption of that particular system. On the other hand, the items relating to terminals are completely missing.

It is not possible to set the number of terminals and their performance. They can be inserted as elements within the model, but they do not interface with the plug-in during the calculation. For a more accurate analysis, it should be necessary to use an external program that includes these attributes. In general, the number of parameters that can be selected is less than the parameters available in the most common software for energy analysis.

For natural ventilation, the parameters to be set are accompanied by an hourly schedule. In this case, it was set to keep the "system" active all year round. In the case of mechanical ventilation, it is certainly useful for calibrating the best usage profile. In addition, it is also necessary to specify the air changes by choosing from four different units of measurement. Furthermore, it is possible to select a function that uses automatically the standard ASHRAE values.

Once all the elements of the building and their materials are set, it is possible to conduct the calculation of the thermal bridges. Before proceeding with the calculations, the plug-in updates the model with the latest changes and automatically detects errors or warnings, which, if not resolved, do not allow it to continue with the simulation. Solving any errors and starting the simulation, EcoDesigner STAR compiles a final report of the simulation.

#### *5.2. First Results of the Analysis*

According to the aim of this paper, some advantages and disadvantages were detected already in this step. The main advantages are:


Even if they are not many and do not affect the use of the program, some constraints were found and listed following:


#### **6. Application of the Methodology on an Existing Case Study**

In order to verify their correctness, the procedures for the energy analysis in the BIM environment, developed on a sample building, have been tested on a sample building. Indeed, only a building complete in all its parts can provide the necessary information, especially from the plant engineering point of view. Furthermore, in an existing building, the systems have already been measured and have precise characteristics. They can be traced back by recovering the technical sheets drawn up by the manufacturers. The research of the case study was conducted by preliminarily defining some characteristics that the case study building must possess. The aim was to validate the methodology and the final results of the energy simulation. These features are:


#### *6.1. Case Study Modelling*

As said, this case study was selected as an important sample of existing architecture characterized by the main data available, and, as well in this case, for simplicity. This latter concerns both the spatial and architectural quality of the building or housing, and the relationships with the ground and the characteristics of use that allow more effective control of the BEM energy analysis. The chosen building, in fact, has simple thermal zones, with no particularities (of use or construction) that can alter the calculation of the energy simulation. It is characterized by a simple structure, and a regular plan, with essential thermal zones (with a common residential type of user profile). It is a residential building and has three apartments distributed over three floors above ground connected by an external staircase. Only the apartment on the first floor was chosen.

It must be specified that some simplifications regarding the articulation of the architectural and climate artefact can be made, where necessary, to ensure greater control over data processing by the software. Moreover, some simplifications, e.g., regarding the articulation of the architectural and climate artefact, were made. It endured greater control over data processing by the software.

The selected building met all the necessary characteristics: The Langham House Close residential complex in Richmond (England), designed by James Stirling.

The apartment building consists of 18 residential units. They are spread over three elevations above ground (6 per floor). There are 3 different types of apartments, all based on the same floor plan, which differ in the number of rooms:

• 3 apartments with one bedroom (approx. 65 m<sup>2</sup> );


The three smaller apartments, all on the ground floor, are identical to the apartments with two bedrooms. One of the rooms is intended for the service of condominiums, as an accessory storage. There are also three two-bedroom apartments on the ground floor. On the next floor, there are three two-bedroom apartments and three three-bedroom apartments; the same distribution is repeated on the second and last floor.

At each level, a pair of housing units are served by a common stairwell; one block includes 6 apartments on 3 levels, served by a stairwell; the residential complex consists of 3 blocks (Figures 6 and 7).

**Figure 6.** Plans of the building.

**Figure 7.** Section of the building.

The complex is located in a suburban area of London, characterized by a low population density and a strong natural presence; the residential complex is open on all fronts and is surrounded by trees [44].

The entire structure is in load-bearing masonry, consisting of solid bricks (215 × 102.5 × 65 mm) and 10 mm lime mortar joints. The wall structures are differentiated into three types:


Each apartment is characterized by the presence of a central block, where the fireplace is located, and the plant room; this block is in load-bearing masonry and plays a decisive role in the load-bearing structure of the building.

Regarding the structure, the load-bearing masonry is combined with reinforced concrete beams characterized by a rectangular section (27 × 35 cm). They work as curbs for the distribution of loads of the upper floors. Outside, the elevations are characterized by the alternation between brick and concrete. In addition to the beams, visible directly from the outside, there are other reinforced concrete elements, such as the U-shaped gargoyles, the ventilation openings, and the panels under the windows. These latter serve to further stiffen the floors, linking the masonry with the reinforced concrete of the beams and floors [45].

The floors are reinforced slabs, made together with the beams and panels under the windows. Their stratigraphy, described from bottom to top, varies according to the reference plane:


The fixtures are very similar to those envisaged in the project and consist of wooden frames, painted white, with single glass.

Regarding the plants, it must be noted that this building, like many others built in the 1950s, was not originally equipped with heating or cooling systems. The control of the internal temperature was therefore obtained through natural ventilation. In the winter season, the heating of the rooms was performed by a wood-burning fireplace in each single house in the living area. Over the years, with the change in technology, product costs, and lifestyles, each home has been equipped with a heating system. The individual owners carried out the construction of the systems independently. For this reason, the components of the systems (boiler, radiators) vary from apartment to apartment. The solution adopted provides, in general, the installation of an autonomous internal boiler of about 24 kW in the central masonry body. The boiler allows the production of domestic hot water and the power supply of the terminals located in each room of the apartment. The terminals are standard radiators. Given the differences between the apartments, a schematic was adopted in the calculation phase, considering the system of a typical accommodation and then applying it to the other apartments examined. Therefore, small differences (model or commercial brand of the radiator, for example) were eliminated, given that they did not affect the calculation results.

The fireplace is present in all apartments, but its function changes, e.g., in some cases, it is not used. Its function is performed by the heating system (Figure 8). In other cases, residents decided to continue to use it in combination with the heating system, maintaining the wood supply, and others replaced the wood-burning fireplace with a gas fireplace that replaces the radiators in the living area.

**Figure 8.** Pictures of the flat with a gas-fireplace and a wood-fireplace (**above**) and a picture of the external part of the building with the analyzed part (**below**).

Figure 8 shows pictures of the flat with a gas-fireplace and a wood-fireplace and a picture of the external part of the building with the analyzed part. The cooling systems have never been installed because they are not necessary because the climate in London in the summer is not very hot. Furthermore, it should also be considered that the building is located in a well-ventilated area, far from the densely built urban center.

The present study examined a single block consisting of 6 apartments, distributed in pairs, on three elevations, and served by a common staircase. The limitation to a single block of apartments does not affect the search results. It respects the modularity desired by Stirling and excludes the repetition of identical elements, superfluous for the purposes of the calculation. The delimitation required the modification of the perimeter walls of the housing: The walls that previously bordered other apartments, in solid bricks, were transformed into cavity walls bordering the external environment, as is already the case for the rest of the construction.

The modelling process was conducted on the basis of two-dimensional graphic references (plans, sections, elevations) produced through the redesign of the project drawings and the verification of the relative congruence (between plan and section, for example); the modelling phase was conducted, as required by BIM, specifying the material and construction characteristics of the individual elements and also the parameters useful for the energy simulation. An accurate BIM model of the building, defined in its architecturalconstruction aspects, was developed. From the model, it is possible to extract plans and sections, or inspect the building in three-dimensional views.

To export the model from the Building Information Model (Figure 9) and to import it into the Building Energy Model, it is necessary to create the thermal zones to which each room is assigned. This operation, easy and immediate from an operational point of view, requires particular attention from a conceptual point of view.

**Figure 9.** Navigable virtual model, view of the main front.

For the Stirling building, 7 thermal blocks were identified (Figure 10), one for each of the 6 apartments, and one for the common areas. The thermal block referred to an apartment, contains within it as many thermal zones as there are rooms that compose it. Indeed, all the rooms are heated by the same system and therefore share the same internal temperature. Furthermore, since this is a residence, the occupancy will also be homogeneous throughout the apartment.

**Figure 10.** Graphic visualization of the Building Energy Model (BEM) model with chromatic distinction of the thermal blocks.

Common areas do not have any type of heating and have a different occupancy profile from that of the apartments. After verifying the correct definition of the Building Energy Model, it is possible to proceed to enrich its information content. The operations carried out within the three EcoDesigner STAR tabs are: Thermal Blocks, Structures, and Openings. Within the thermal block section, it is possible to modify the operation profiles of each thermal block with related heating and ventilation systems, to determine, during the calculation phase, the thermal inputs deriving from external and internal factors. The analysis on the operation profiles was calibrated on the basis of a typical English family. The schedules were set including the differentiation of the types of use over the different seasons and working and non-working days. To identify the period of operation of the heating system, the graphs on the climatic data generated by the software were examined. It is thus determined that the apartment is inhabited for a few hours a day during work and school days and that, consequently, systems, lights, and appliances will be active for a few hours. On the other hand, the occupation during non-working days is different, when the apartment is occupied for most of the day, generating a more intensive use of systems, lights, and appliances.

A deep study made it possible to trace in detail the characteristics and technical data sheets of the elements that compose it (Figure 11). Similarly to the operation profiles setting, heating and ventilation systems must be assigned to each thermal block.

—

**Figure 11.** Schedules of the operation profiles. Note how the occupation of the houses changes between working and school days (**left**) and non-working days (**right**) within the period of the year in which the heating is activated.

" "

EcoDesigner STAR, as already specified above, does not provide for the insertion of the heating system terminals (radiators); therefore, only the parameters relating to the boiler (nominal power and flow and return temperatures) and the production of domestic hot water have been entered. Figure 12 shows the window in which the flow and return temperature of the heating system water is set and the energy source used to power the boiler.

**Figure 12.** On the left, the window in which the flow and return temperature of the heating system water is set; on the left, the energy source used to power the boiler.

— The ventilation of the apartments is natural. Therefore, it was possible to set parameters such as the number of air changes per hour (defined as ACH—Air Changes per Hour) and the program for using the fixtures. The air exchange has been set with a value of 0.5 ACH. For the attribution of this value, the harsh climate was considered.

" " The tab called "Structures" can be used to insert and calculate thermal bridges. The software manages the calculation of the thermal bridge in an extremely intuitive way:


This calculation provides an interactive graph of the temperatures (or a graph of the heat flow). The main thermal bridges identified in the analyzed case study concern the combination of bricks and concrete beams/panels of reinforced concrete, and the material discontinuities at the windows and the corners of the structure. The window fixtures required a detailed study for each frame. Each window was decomposed optimally to not distort the performance of the building envelope. In order to conduct this analysis, the factors considered are: The juxtaposition between the concrete panel and the masonry wall, the particular shaped frame and its contact with the bricks and concrete, and the angle of the structure, which is identified as a thermal bridge in shape. Normally, these aspects should be taken into account separately, but their positioning within a very small area does

not allow this procedure. In doing so, thermal bridges would be calculated two or more times, in the elements to which they belong and in the adjacent ones. It should distort the performance of the building envelope, which would be worse than they really are. It was, therefore, decided to break down and schematize these factors. In this way, they were considered independent, and a correct calculation was obtained.

The first thermal bridge calculated was that between the reinforced concrete panel under the window and the adjacent masonry. In this case, the thermal bridge of the shape deriving from the angle formed by the structure (the wall to the right of the panel) is calculated simultaneously. The thermal bridge was divided into two parts: One between window and brick and one between window and concrete. The resulting thermal bridge value has attributed a length equal to the perimeter of the window in contact with the bricks, excluding that part in contact with the wall in the right corner. It was because the effect of this thermal bridge was calculated in the case of the previous step.

The thermal bridge of the window was calculated in contact with the concrete, considering a vertical section of the frame (Figures 13 and 14). The length to be attributed to the thermal bridge is therefore the perimeter of the frame in contact with the concrete.

**Figure 13.** Output of the calculation of the thermal bridge of the window inside a fictitious wall (**left**) and dimensions considered for the length to be attributed to the thermal bridge (**right**).

**Figure 14.** Output of the calculation of the thermal bridge of the window in contact with the concrete (**left**) and dimensions considered for the length to be attributed to the thermal bridge (**right**).

In this calculation, the floor in contact with the beam was also included because it gave rise to another thermal bridge. By calculating the thermal bridge of the concrete beam in contact with the floor, the lengths already taken into consideration for the frames were excluded. Once all thermal bridges were calculated, they were attributed to each thermal block via the structures table. Alternatively, it is also possible to enter a table value of the thermal bridge, but the specific calculation for each element is always to be preferred. In this last sheet, the characteristics of the frame are specified. They can be selected from a vast library inside the plug-in, divided into glass type and frame material. The alternatives made available by the library are numerous and can satisfy even the cases of fixtures with particular performances. However, if the characteristics to be entered do not correspond to those present in the library, it is always possible to manually overwrite them for each frame or groups of frames. It is also necessary to start the calculation of the solar analysis (Figure 15) for all external frames (all internal doors will be automatically excluded). This operation has a double advantage. The first is that the data obtained can be used by the program in the calculation phase, while the second is that the professional receives support, during the design, from the interactive graph produced as a result of the calculation. This graph offers the possibility to investigate, day-by-day, hour-by-hour, the irradiation conditions of a given frame. So, it is possible to instantly evaluate the effectiveness of the positioning, dimensions, or shielding system adopted (Figure 16).

**Figure 15.** Three-dimensional display of the selected window element and relative solar analysis.

Before proceeding with the energy simulation of the building, it is possible to select a "Reference building" in the calculation phase. This building is another virtual model that serves as a benchmark. Thanks to it, the advantages and disadvantages of the two alternatives of the same project can be immediately highlighted. Moreover, it is possible to compare the building under consideration with a similar one whose performance we already know. This is a completely optional operation. The simulation can very well proceed with the building data without any reference building.

However, in this case, it was decided to build a reference building model both for completeness in the study of the possibilities of this software, and to highlight considerations on the design applications extensively presented in the next paragraph. The construction of the reference building was based on the same geometric-architectural model of the case study building. The elements and climatic data used are completely identical; while thermo-physical properties of the elements are different.


**Figure 16.** Window for selecting openings/frames on the left, library of the glass type and frame material on the right.

" " " — " " — " " " In particular, the calibration of the building envelope was based on the guidelines dictated by the "Interministerial Decree of 26 June 2015—A" and by the "Interministerial Decree of 26 June 2015—B" [46]. The procedure adopted is purely for study purposes, as the building was not designed in Italy nor is it subject to Italian regulations. The parameters are classified in the standard according to the climatic zones of the locality. Italy is divided into 6 climatic zones ranging from zone A to zone F [47]. They differ in the value of degree days (GG). The "degree-day" is defined as the sum, over one year, of the (positive) difference between the internal ambient temperature and the average daily external temperature [48]. The indoor temperature in Italy has been set at 20 ◦C, so the degree days are calculated based on this temperature. It is evident that the Stirling building cannot be placed in the Italian climatic zones, therefore the problem arises of which climatic zone to choose to obtain the parameters of the reference building. It was decided to calculate the degree days near London (Ham Common, Richmond, BC, USA). In doing so, also the fictitious climatic zone according to Italian parameters was found. Although in England the degree days (HDD and CDD, respectively, Heating Degree Day and Cooling Degree Day) are used, they are calculated differently than the Italian ones, and in particular, they refer to an internal temperature of 15.5 ◦C instead of 20 ◦C. Comparing the GG calculated with two different temperatures is an operation that distorts the results at the start, it was necessary to calculate them through an online application [49]. It was used to choose the internal temperature to be included in the calculation. The output of this process is a spreadsheet in which the following are entered: The period of time considered and temperature of the reference indoor environments, source, accuracy of the climatic data for that area, weather station used, a table with monthly degree day values, and finally the total. A value of 3063 GG was considered. It was compared with the parameter suggested by the Italian standard. So, the Stirling building was ideally placed in the "climate zone F". It indicates

the period of heating system operation and it allows identifying the parameters that the building envelope must have to be considered as a reference building. EcoDesigner STAR can overwrite the new parameters on the old, as a finished element, to quickly update the Building Energy Model in a few steps. This avoids replacing the elements built previously (walls, floors, windows, etc.) and having to model the building again. The resulting building is better performing than the real building. It is because the building envelope is made up of elements with high thermal efficiency. After starting the energy simulation calculation, it is possible to save the building as a reference building. Finally, it can be used as a term of comparison when analyzing the building with real parameters.

#### *6.2. Simulation Results*

All the aspects studied and exposed were useful for the correct construction of the BEM model and a valid setting of the data for energy simulation. The previously built reference building was included in the appropriate tool section. At the end of the calculation, a final report was obtained.

The first section reports all the general data of the entire building, including geometric ones, and some average values for all the thermal zones. The data concerning the energy supplied to the building (heating system, lighting systems, internal heat inputs due to the presence of people, etc.) are shown in the form of a weekly chart. It provides an immediate understanding of which system requires the most energy. Therefore, it predicts which of these fields can be the most expensive in economic terms. The first section contributes to giving an overall view of the analyzed construction. The second section contains the same information as the first, but this time concerning the individual thermal blocks. Each block is associated with a weekly graph of the energy supplied and a graph on the energy emitted. In the specific case of the Stirling building, the 6 apartments are shown (each definitive with a thermal block) and a single common area (represented with a single thermal block). It was noted that the apartments that require less energy in a year are those on the first floor. It is because they exchange heat with the external environment only on three sides. On the fourth side, the apartments border an unheated internal environment. Both at floor and ceiling level they border with other heated rooms. Therefore, the heat losses are less. The accommodations that require more energy to maintain an optimal internal temperature are those on the top floor. Indeed, they exchange heat with the external environment even from the ceiling. Moreover, the roof slab, compared to the floor slabs, has a lower thickness and there is not any type of insulating layer. The apartments on the ground floor, on the other hand, have energy consumption closer to those on the first floor. Another aspect that can be immediately noticed is that the apartments on the right of the entrance (and therefore facing north) have slightly higher energy consumption than those facing south. The third section relates to the daily temperature profiles. It is possible to insert graphs for each day of the year of any thermal block, to show the curves of internal and external temperatures. During the design phase, these graphs are very useful to better calibrate the systems and know when it is necessary to heat or cool the rooms. The fourth section is dedicated to the energy consumption, environmental impact, and energy production (if renewable energy sources are present). Energy consumption is shown both as a table and as a graph, and is divided into categories (heating, cooling, domestic hot water production, consumption due to mechanical ventilation if present, lighting, and equipment). If the prices of the various energy sources are also set, the cost of the various systems can be known. The environmental impact is instead calculated through the kg/y of CO<sup>2</sup> emitted, or the carbon dioxide expressed in kg emitted in a year.

The last section focuses on comparing the consumption of the building to be analyzed and a reference building. It is possible, through this section, to compare two variants of the same project, in order to know both the consumption of the two buildings. They are compared in economic terms.

In Table 2, the results of the simulation were reported.


**Table 2.** Geometric characteristic of the analyzed apartment and simulation result ante and post ideal retrofit action.

The proposed interventions are to be considered the hypothesis that complete and conclude the entire process outlined above. It has to be remembered that the aim of this paper is not the design of improvement solutions for the James Stirling building, but the definition of a methodology that integrates the simulation energy in the BIM environment, thus identifying a valid design support tool. Anyway, some possible interventions aimed at reducing the consumption of individual apartments and improving the performance of the entire building are listed below:


By updating the BIM model with the new parameters, the BEM model will also be updated automatically. So, the energy simulation calculation can be quickly started.

The consumption of the apartment at the ground floor after the application of the retrofit actions is 12,243.32 kWh; while before the ideal retrofit actions was 16,349.43 kWh.

The apartment on the first floor adjoins two heated rooms, both at floor and ceiling level, with an annual consumption of 14,766.44 kWh, before the ideal retrofit actions, and of 11,741.08 kWh, after the ideal retrofit actions.

The consumption of this apartment is slightly lower than the ground floor apartment and significantly lowers. The apartment on the second level borders on a heated room at floor level and with the external environment at ceiling level. The annual consumption calculated was 22,539.95 kWh before the ideal retrofit actions, and 13,873.37 kWh after the ideal retrofit actions. Furthermore, it is possible to obtain the perspective sections of the apartments in which it is also possible to read the distribution of temperatures within the building envelope; this type of paper, following a correct interpretation, is particularly useful for identifying the areas of intervention.

Looking at the thermography of the new configuration of the building, it can be seen how the roof slab disperses less heat than the real configuration and how the windows and cavity walls break down the heat flow.

#### **7. Results**

In the previous section, the results of the process were reported. By conducting this study, it was possible to highlight the advantages and limitations of the tool EcoDesigner STAR application and to outline a clear picture of the potential and criticality of the chosen. The greatest advantage that is obtained from the use of ArchiCAD associated with EcoDesigner STAR is the overcoming of one of the major problems in this field, namely the transition from BIM to BEM. The modelling software allows the construction of a BIM model according to certified tools. Plug-in allows its interpretation in BEM with an almost instantaneous operation.

It is clear that this transition from BIM to BEM is error-free. Moreover, it is configured as an immediate operation when the designer builds the BIM to then conduct an energy analysis. Indeed, during the design and modelling process, it is necessary to better calibrate the data entered in the BIM, the used elements and the relationships established with other elements. It is also useful to facilitate the subsequent transformation of the Building Information Model Building Energy Model. It has to be reminded that, according to the BIM goals, the interoperability must take place from the earliest stages of design. To do this, all the professional figures required should be involved to operate with the same objectives and making the work faster and more effective.

If the BIM model is built to transform it into BEM, the operation is easy. In this way, it is possible to apply an energy simulation in all the design phases of a building. Clearly, this action occurs with degrees of detail and accuracy based on the progress of the project. So, better integration between design and energy analysis can be achieved. This latter is not relegating to the final phases of the project. As for typical energy analysis, at the end of the simulation process, many data can be obtained (both directly and indirectly). From them, it is possible to consider possible interventions aimed at reducing the consumption of apartments.

Another advantage of this tool is the feasibility to compare two or more variants of one of the same building. They can be compared both in the early stages of the project and in the final stages, managing to choose the best solution according to needs.

The output data looks user-friendly. It is an important aspect for the designer, who can know the advantages and disadvantages of a design choice almost immediately. For example, it is possible to choose the orientation and materials of the building envelope and start an initial energy simulation by mentioning the other fundamental data. Furthermore, it is possible to propose a different orientation and different types of materials for the building envelope, keeping the other data completely identical to the previous variant. Finally, it proceeds with the simulation of a second alternative and automatically compares it with the first, in order to obtain graphs on the savings and consumption of both and choose the most suitable solution. In order to support this consideration and to check the possible improvements of the tool, further comparison with a stand-alone software

was performed. The main results are reported in Table 3. Regarding the building systems, "basic" means that there are just a few options to set the plants; "detailed" means that it is possible to set all the parameters. " " " " " " " "


**Table 3.** Comparison with Termolog software. " " " "

#### **8. Discussion**

' ✕ ✓ ✓ ✓ ✓ ✓ ✕ ✓ ✓— ✓— ✓ ✓ ✓ ✓ ✕ ✓ ✓— — —Efficiency [η] ✕✓— ✓— ✓— ✓— ✓— ✓— ✓ ✓ ✓ ✕ ✓— — ✓— Efficiency [η] ✕ ✓— ✕ ✓ ✓— ✓— ✓— ✓— ✓— ✓— The main goals of this study are the analysis of the interaction between BIM and energy simulation, through a review of the main existing commercial tools, and the identification and application of a methodology in a BIM environment by using Graphisoft's BIM software Archicad and the plug-in for dynamic energy simulation EcoDesigner STAR. The application on a case study gave the possibility to explore advantages and limits of this commercial tools and, consequently, to provide some possible improvements.

✕ ✓

✕ ✓ ✓— ✓—

✓ ✕

✓— ✓— ✕ ✓

✓ ✓ ✕ ✓

✓ ✓ ✓ ✓

'

'

' ' ✓ ✓ ✕ ✓ ' ✕ ✓ ✓— ✓— ✓ ✓ ✓ ✓ ✕ ✓ ' As said in Section 4, the selection of the analyzed tool was based on some main criteria. The first characteristics are the versatility and the possibility to combine BIM modelling with well-integrated plug-in for energy analysis, to import and export building 2D drawings, BIM modelling and 3D visualization, quasi-static energy diagnosis, dynamic energy diagnosis, calculation of thermal bridges, and calculation of renewable energy sources. The second characteristic is that it meets the requirements set by the most advanced energy diagnosis standards, such as UNI EN ISO 52016-1 for the calculation in dynamic hourly regime, and ASHRAE 140-2017 and UNI/TS 11300 on the monthly average stationary calculation. Finally, it meets the validity requirements required by buildingSMART for IFC certification. All these requirements, according to the conducted study and the results reported above, were confirmed.

Although the results of this study are satisfactory, some critical issues and disadvantages were found within the application and possible fields. They can be addressed in future research, in order to improve digital tools and achieve perfect integration between BIM and BEM, without the passage of digital models in third-party analysis software. As shown in Table 3, the major limitation that has been detected is the poor personalization of data relating to heating systems. The type of data that can be set is limited and inherent to the fundamental characteristics of the system, such as the nominal power, the thermoregulation, the nominal capacity, and the COP/EER. It is not possible to specify the efficiency and to insert the heating terminals with their technical specifications. Furthermore, it is not possible to set the vapor diffusion resistance factor of the material, the efficiency and the emission of the system.

Thus, as improvements of the tools there is the implementation of the possibility to set these latter parameters. In particular, giving the possibility to set the vapor diffusion resistance factor of the materials, it will improve the calculation of possible surface and interstitial condensation. Regarding the HVAC system, in order to provide a more precise energy analysis, the tool could be improved by giving the possibility to set the efficiency of the boiler and the emission system characteristics and typology. Furthermore, it could be necessary to detail the parameters to be inserted regarding the nominal capacity, the thermoregulation and the COP/EER. Finally, some details about the lighting system and mainly the control system should be added.

#### **9. Conclusions**

The purpose of the paper is to study a new integrated energy simulation methodology in a BIM environment by using Graphisoft's BIM software Archicad and the plug-in for dynamic energy simulation EcoDesigner STAR. They have been selected after a careful study of the rules on energy analysis, an examination of the operational potential of different software on the market, and research conducted by a wide scientific community interested in various capacities in issues related to the interaction between architecture and energy analysis. Thanks to the application on the case study, the advantages and disadvantages of the existing tools were highlighted and compared. The greatest advantage that is obtained from the use of ArchiCAD associated with EcoDesigner STAR is the overcoming of one of the major problems in this field, namely the transition from BIM to BEM. The modelling software allows the construction of a BIM model according to certified tools. Plug-in allows its interpretation in BEM with an almost instantaneous operation when the designer builds the BIM with the intention of conducting an energy analysis. Another advantage of this tool is the feasibility to compare two or more variants of one of the same building, both in the early stages of the project and in the final stages, managing to choose the best solution according to needs. Furthermore, the output data looks userfriendly. It is an important aspect for the designer, who can know the advantages and disadvantages of a design choice almost immediately. Moreover, some critical issues and disadvantages were found within the application and possible fields. The major limitation that has been detected is the poor personalization of data relating to heating systems. The type of data that can be set is limited and inherent to the fundamental characteristics of the system, such as the nominal power, the thermoregulation, the nominal capacity, and the COP/EER. It is not possible to specify the efficiency and to insert the heating terminals with their technical specifications. Furthermore, it is not possible to set the vapor diffusion resistance factor of the material, the efficiency, and the emission of the system.

These lacks can be used as a starting point for further improvements to the tool. The possibility to set the vapor diffusion resistance factor of the materials would improve the calculation of possible surface and interstitial condensation. Regarding a more precise analysis, the tool could be improved by giving the possibility to set the efficiency of the boiler and the emission system characteristics and typology and to detail the parameters to be inserted regarding the nominal capacity, the thermoregulation, and the COP/EER. Finally, some details about the lighting system and mainly the control system should be added.

**Author Contributions:** Conceptualization, M.B., S.D.L. and G.L.; methodology, M.B., S.D.L. and G.L.; software, S.D.L.; validation, M.B., S.D.L. and G.L.; formal analysis, M.B., S.D.L. and G.L.; investigation, M.B., S.D.L. and G.L.; data curation, S.D.L.; writing—original draft preparation, M.B., S.D.L. and G.L.; writing—review and editing, M.B., S.D.L. and G.L.; visualization, M.B., S.D.L. and G.L.; supervision, M.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** This study starts from the degree thesis [50], carried out by Simone Di Lisi with the fundamental support of Arch. Fabrizio Agnello and of the Ing. Marco Beccali.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

