Next Article in Journal
Humidifier Disinfectant Consumption and Humidifier Disinfectant-Associated Lung Injury in South Korea: A Nationwide Population-Based Study
Next Article in Special Issue
A Study on the Willingness of Industrial Ecological Transformation from China’s Zero Waste Cities Perspective
Previous Article in Journal
The Associations between Sleep Duration, Academic Pressure, and Depressive Symptoms among Chinese Adolescents: Results from China Family Panel Studies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 11
10.3390/ijerph18116135
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry—A Bibliometric—Qualitative Literature Review of Research Activities

by
Bilal Manzoor
1,
Idris Othman
1 and
Juan Carlos Pomares
2,*
1
Department of Civil & Environmental Engineering, University Technology PETRONAS, Seri Iskandar 32610, Perak, Malaysia
2
Civil Engineering Department, University of Alicante, P.O. Box 99, E-03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(11), 6135; https://doi.org/10.3390/ijerph18116135
Submission received: 3 May 2021 / Revised: 3 June 2021 / Accepted: 4 June 2021 / Published: 6 June 2021
(This article belongs to the Special Issue Operational Safety Economics: Challenges and Advances in Methods and Applications in the Process Industries)
Browse Figures
Review Reports Versions Notes

Abstract

:
Digital technologies (DTs) are proven helpful in the Architecture, Engineering and Construction (AEC) industry due to their varied benefits to project stakeholders, such as enhanced visualization, better data sharing, reduction in building waste, increased productivity, sustainable performance and safety improvement. Therefore, researchers have conducted various studies on DTs in the AEC industry over the year; however, this study explores the state-of-the-art research on DTs in the AEC industry by means of a bibliometric-qualitative review method. This research would uncover new knowledge gaps and practical needs in the domain of DTs in the AEC industry. In addition, bibliometric analysis was carried out by utilizing academic publications from Scopus (i.e., 11,047 publications for the AEC industry, 1956 for DTs and 1778 for DTs in the AEC industry). Furthermore, a qualitative review was further conducted on 200 screened selected research publications in the domain of DTs. This study brings attention to the body of knowledge by envisioning trends and patterns by defining key research interests, journals, countries, new advancements, challenges, negative attitudes and future directions towards DTs in the AEC industry. However, this study is the first in its vital importance and uniqueness by providing a broad updated review of DTs in the AEC literature. Furthermore, this research laid a foundation for future researchers, policy makers and practitioners to explore the limitations in future research.

1. Introduction

The Architecture, Engineering and Construction (AEC) industry is undergoing a significant shift from conventional labor-intensive methods to automation through the use of digital technologies (DTs) and has played a significant role in this revolution [1]. In general, DTs generally refer to information and communication technologies (ICTs) that facilitate the development, storage and handling of information and promote the various forms of communication between human beings and electronic systems and between electronic systems in digital binary computing systems [2]. With the great emphasis on these technologies in the AEC industry, the assessment of their impact on user behavior is becoming increasingly important as DTs transform the relationship between the construction process and human behavior [3]. As a result of growth in the concepts of digitalization and automation in Industry 4.0, DTs have gained rising attention [4,5]. They could be used for logistics operations, near-real-time knowledge flows, end-to-end supply chain consistency and increased human interaction through the application of digital technology, in particular labor-intensive activities [6].
Building information modeling (BIM) [7], augmented reality (AR) [8], virtual reality (VR) [9], photogrammetry [10], radio frequency identification devices (RFID) [11], geographic information systems (GIS) [12], global positioning systems (GPS) [13], wearable safety devices [14], quick response code (QR) [15], artificial intelligence (AI) [16], robotics [17], block chain [18], onsite mobile devices [19] and laser scanning devices [20] are all seen as promising DTs in the AEC industry. In general, the advantages of using DTs in the AEC industry are known to be immense. For instance, AR-VR technologies have shown promising results in a number of fields related to the AEC industry offering solutions to collaborative and communication issues [21]. Methodologies such as BIM for project documentation and architecture management enable teams to work on a cooperative network, collaborate and share project data [21]. Various examples of VR integrations assembled for applications associated with the AEC show promising results, e.g., urban planning [22], acoustic analysis [23], design analysis and decision-making support [24], construction safety [25], enhancement in spatial perception and learning results in engineering related areas [26]. Furthermore, RFID can help boost the transparency and productivity of the supply chain [27]. Likewise, for data collection, tracking, visual monitoring and assessment purposes, GPS–GIS technologies can be used [28]. While robotics use has the potential to increase efficiency and safety, it should not inherently in the longer term reduce total work opportunities in the construction sector [29]. Using the camera of a smartphone, an application reads the QR code and shows the clash-free model on its display. Users can incorporate, interpret or use section planes to easily visualize the design of the drawings [30]. In addition, AI offers huge potential for substantial efficiency improvements through rapid and accurate analysis of large data volumes [31]. In addition, AI systems and technologies can resolve complex, non-linear functional issues and once equipped, predictions and generalizations can be carried out at high speed [32].
With the increasing demand for DTs in the AEC industry, more studies are needed in this field. However, previous review studies in this field [16,33,34,35] have made significant contributions but have various limitations. Firstly, they have been used manual methods [36]. Secondly, there is a lack of comprehensive reviews of state-of-the-art research covering all the aspects of DTs under one umbrella. For instance, Ibem and Laryea [35] conducted a literature review that only focus on the procurement of construction projects. Guo et al. [34] conducted a literature review but only limited to the boundary of construction safety. Pauwels et al. [27] conducted a literature review but limited to the benefits of semantic web technologies. Darko et al. [16] conducted the first comprehensive scientometric study assessing the state-of-the-art of research on AI in the AEC industry, but limited within the domain of Al. In the context of this evidence, these analysis studies do not provide a complete picture of state-of-the-art research on DTs in the AEC industry.
In fact, there is still a lack of a study that provides a detailed image and understanding of the literature of DTs in the specific domain of AEC. Therefore, the objectives of this study are: RQ1. What is the annual publications trend on DTs in the AEC industry? RQ2. Which journals publish studies on the contribution of DTs in the AEC industry? RQ3. In which context (country) were the studies conducted? RQ4. Which digital technology emerges the most among all other DTs? RQ5. What are the new advancements, challenges, negative attitudes and future directions towards DTs in the AEC industry?
In order to overcome this research gap, a quantitative (i.e., bibliometric analysis) and qualitative approach (i.e., systematic review) were used to seek responses to these questions for the benefits of academia and industry. It is anticipated that the answers to these questions would give rise to contributing knowledge in literature and DTs. As a consequence of DTs are gaining broader market development and the interest of practitioners, construction stakeholders and researchers in the global environment to benefit from new advancements and future directions.

2. Background of Digital Technologies

Four phases in the evolution of DTs have been addressed in literature studies. The first step was pre-1838 when Samuel Morse invented the first telegraphic transmission and manually transmitted information. Following this, electricity and electro-mechanical control were invented, and gadgets such as the telegraph, telephone, radio, television were developed. The third step was the convergence, separately used, of broadcast and electronic technology in the 1950s and data were transmitted in analogue mode before the 1950s. The final step was to replace the analogue mode of data transfer with the digital system [37]. In the 1960s, electronic devices and signal processing were introduced and gradually accelerated as digital goods such as CDs were introduced into the market in the 1980s. It is evident from this that ICTs were incubated for more than 100 years, while their digitalization, i.e., the process of translation into electronic and binary machine language by sound, text, voice or image, was taken 20 years and so on to execute [37].
In particular, the digitalization of ICTs increases the ability of network technologies, the performance of data and information exchange and cost prosperity in the storage, transmission and processing of information [38]. The ability of DTs to integrate electronic networking and computing in data processing and manage various data sorts simultaneously (e.g., text, sound or image) has thus led to the provision of computer equipment, telecommunications systems and networks that have problems [39]. Thus, the three basic characteristics of DTs: integration and multi-functionality; intelligence and prevalence are undoubtedly very important features; where good data processing, powerful connectivity of tasks and procedures, clear communication, coordination and collaboration of the workflow are needed [40].
In addition, DTs have various applications such as communication, computing accessories, networking gadgets, intangible items, software applications, communication networks and the Internet. DTs can also promote the execution of a wide range of enterprise and natural processes, including manufacturing. [41]. For their applications, therefore, DTs can be divided into six key classes. These include data and information capture, storage, processing, collaboration and display, as well as for integration, collaboration and synchronization of work processes with storage, collaboration and interoperability service abilities; for example, keyboards, floppy discs, hard discs, mobile devices, printers, fax machines, readers bar code and scanning images [42].
Since its inception, the AEC industry has pursued ways to reduce costs, improve efficiency, enhance visualization, improve data sharing, reduce building waste, increase productivity, sustainable performance, improve safety and boost quality, all while attempting to reduce delivery time. The AEC industry is still heavily reliant on conventional drawings and procedures in order to conduct business [43]. Simultaneously, the AEC professionals recognize the value of DTs in achieving more precise and intelligent modeling. Since the AEC industry is highly competitive, those AEC firms that effectively implement the latest technologies will be able to outperform the competition due to their ability to adopt them. Despite enormous automation opportunities, the AEC sector has only recently begun the transition from tradition to automation [15].

3. Research Methodology

To fulfill the research objectives, a “mixed-systematic review method” was used in this study and comprises of a quantitative approach (i.e., bibliometric analysis) and a qualitative approach (i.e., systematic review). The flowchart of research methodology is shown in Figure 1.

3.1. Mixed-Systematic Review Method

Quantitative and qualitative approaches are collectively referred to as a mixed-systematic review method. The purpose of the mixed approach seeks to use the power of qualitative and quantitative approaches and to reduce the inconvenience [44]. To imagine the systemic and complex dimensions of scientific research, the bibliometric approach is used [45]. Bibliometric visualization is an important tool that visualizes the domain of information and the relationship among papers, journals, and so on [46]. In this study, bibliometric mapping is used to classify knowledge domains and research patterns for DTs in the AEC industry based on established literature. Meanwhile, a systematic analysis is underway to include a holistic view of current research to assess lacunae in the knowledge pool and predict future research directions [47].

3.2. Data Collection

The data acquisition has been conducted with the aid of Scopus rather than Google scholar or web of science due to the following reasons (a) Scopus has a broad range of scientific journal publications relative to other databases [48], (b) Scopus has a much greater indexing mechanism that improves the opportunity to access more recent publications [49], (c) previous studies [16,50] used Scopus instead of other databases due to avoid from the difficulty of checking and replication of publications from various databases. The data collection in this study was achieved by two approaches (a) bibliometric analysis and (b) systematic analysis. The bibliometric analysis provides a platform for deeper insight selection of data regarding DTs in the AEC industry. Table 1 elaborates the searching strategies of literature. The first and second queries in Table 1 were merged in order to retrieve existing literature related to DTs in the domain of the AEC industry. “Digital” OR “technology” OR “technologies” AND “Architectural engineering” OR “Construction engineering” OR “Civil engineering” AND “BIM” OR “Building information modeling” OR “Building information modelling” OR “AR” OR “VR” OR “RFID” OR “Photogrammetry” OR “Images” OR “Videos” OR “Cameras” OR “laser scanning” OR “3D scanning” OR “Robotics” OR “Block chain”.
The Scopus search keyword was set as title/abstract/keywords to extract all publications in their title, abstract or keyword section. In this search, 1778 Scopus records were retrieved. A screening technique was successively conducted with the help of a systematic review in order to boost the findings to the applicable engineering scope. Study papers other than engineering, including medicine and agriculture, were additionally omitted. The source title was analyzed to extract irrelevant papers and conference proceedings by performing another refining. The reason to exclude conference papers is that conference papers did not go through the peer-reviewed process before publication. Now, the number reduced to 445 Scopus records. Further, refining was conducted to read the title and abstract of each individual paper and exclude the irrelevant papers. For example, the papers that were outside the domain of DTs and AEC boundaries and non-English publications. The final outcome was 200 articles from 1975 to December 2020.

3.3. Bibliometric Analysis Tools

There are various bibliometric analysis tools such as Gephi, CiteSpace, Sci2, HistCite and VOSviewer available, each of which has its own potential and abilities. In this study, VOSviewer was used that provides the basic functionality needed to produce, visualize and explore bibliometric networks [51]. For instance, Vilutiene et al. [52] used the VOSviewer tool for bibliometric analysis of BIM for structural engineering. In addition, Santos et al. [53] used the VOSviewer tool for bibliometric analysis and review of BIM literature. The most recent study conducted by Babalola et al. [54] also emphasized the usage of VOSviewer in bibliometric analysis of advances in BIM.

4. Results and Discussion

4.1. Annual Publications Trend on DTs in the AEC Industry (20th and 21st Century)

As compared between the 20th century (1975 to 2000) and the 21st century (2001–2020), the trend of publications on DTs in the AEC industry is high in the 21st century (2001–2020). This is a fact by Robin [55] that DTs have become the most powerful technology tool in the 21st century. Bilal et al. [56] also provide evidence of an increasing trend of DTs in the AEC industry in the 21st century. In the 20th century, during the late 1990s, research in DTs had gained momentum when computational capacity and researchers’ dedication to using DTs in resolving more complex issues in a wide range of fields began to increase [57]. Due to this, the number of publications might had caused a little peak in 1996. However, the trend of publications on DTs in the AEC industry was boosted up in 2014–2020. This increase in the trend of publication may be due to the need and level of adoption in the AEC industry [58]. It shows that the adoption of DTs in the 21st century is increasing and raising the benefits. For example, compared to the 20th century, it is difficult to visualize the construction site. However, photogrammetry has made visualization simpler and quicker in the 21st century. Similarly, the monitoring process of the construction area is made reliable with the help of DTs. Figure 2 depicts the comprehensive annual pattern of publication in the domain of DTs in the AEC industry.

4.2. Journal Contributions on DTs in the AEC Industry

Numerous studies have shown and made clear how important academic journals are to be examined in any field of science [59]. The results suggested that the most important contribution of publications on DTs in the AEC industry by the Automation in Construction followed by Journal of Construction Engineering and Management and Journal of Computing in Civil Engineering. This indicates that practitioners, researchers and students who wish to undertake more research in the domain of DTs in the AEC industry should place a greater priority on Automation in Construction due to its significant contribution in the field of DTs in the AEC industry. The mission of this publishing initiative is to advance the subject through publications that include a wide range of information on all aspects of information technology application in the areas of design, engineering, construction and maintenance and administration of developed facilities. To put it another way, it deals with issues such as automated monitoring, smart control systems, computer-aided design and engineering, etc. The contribution of Automation in Construction indicates the most advanced research in the domain of digital construction. Keeping this in mind, this will help future researchers to explore more in the field of digitalization construction and to improve safety performance. However, it is worth mentioning that aside from Automation in Construction, which has the highest contributions, all the others are the AEC industry journals, though the Journal of Cleaner Production and some of the other Engineering journals (Composite Structures, Buildings, Journal of infrastructure systems, Visualization in Engineering, Construction Innovation, Accident Analysis and Prevention) may include vast area of engineering. The detailed theme of journal contributions on DTs in the AEC industry is shown in Figure 3.

4.3. Bibliometric Analysis

4.3.1. Geospatial Distribution of Research Articles on DTs in the AEC Industry

The network of countries in the field of scientific collaboration helps to identify countries that are particularly interested in the field of related research. A network was developed with the VOSviewer for geospatial distribution and collaboration. This type of analysis is known as “co-authorship”, the unit of analysis is known as “countries”. Figure 4 shows a detailed picture of the countries’ collaboration network.
To achieve the optimum network, the “minimum number of country documents” and the “minimum number of country citations” were both set to 15. It can be seen that the U.S. and China have a strong bond with each other and stand at the top-ranked countries in research on DTs in the AEC industry, followed by Spain and the United Kingdom. Furthermore, the U.S. has also strong collaboration and the biggest contributor in research related to DTs with Germany, Greece, Hong Kong, Canada, South Korea and Israel. The Table 2. illustrates the detailed picture of geospatial distribution of research articles on DTs in the AEC industry.

4.3.2. Author Keywords Co-Occurrence Analysis

With the aid of VOSviewer, it is possible to build up the knowledge domain of DTs in the AEC industry by using author keywords co-occurrence analysis by using 200 selected papers. The performance of the VOSviewer (i.e., Network Visualization) is a distance-based map in which the spacing objects represents the strength of the relationship between the items [46]. The representation of various groups of items is shown by different colors that are clustered by the clustering technique of VOSviewer [60]. The node sizes demonstrate the occurrence frequency of the relevant keywords, the arcs simply show the co-occurrence relationship between the keywords, and the line thickness indicates the strength of each relationship [61,62,63].
Various studies also support the use of author keywords for bibliometric analysis [53,64,65,66,67]. Most recent studies such as BIM for structural engineering [52], construction education [68], BIM-based research in construction engineering [69], mapping the knowledge domain of BIM [70] and integrating BIM with building performance [71] also had the recommendation to use this approach. Furthermore, identical terms such as BIM, Building information modeling, Building information modelling and information technologies, information technology, technology were merged as BIM and information technology, respectively. The detailed analysis of keywords with occurrences, average publication years, links and total links is elaborated in Table 3.
Average publication year means average time period in which specific research areas have received attention from the researchers. For instance, photogrammetry, computer vision, terrestrial laser scanning, buildings, displacement measurement and education gained attention in 2016, while studies focused on BIM, civil engineering, unmanned aerial vehicle, case study, LiDAR and structural health monitoring gained attention in 2017. The “links” are the number of connections between a particular items and another, while the “total links strength” represents the total strength associated with particular item [46,72].
There are six clusters omitted on the basis of Figure 5 which are summarized as follow:
(i)
BIM Integrating Cluster (Green Color)
In this cluster, BIM is integrated with architectural engineering, text mining, case study, education, and curriculum. BIM has the potential to reduce rework for various engineering goals and to enhance decision-making in different aspects in the AEC industry [73]. Various studies provide the recommendation of BIM research using text mining techniques [74,75,76]. A recent study conducted by Pan and Zhang [77] for mining BIM log data with the approach of Long Short-Term Memory Neural Network (LSTM NN). Although this research had major contributions but had a limitation to implement it as an Autodesk Revit plugin for a better user experience. Furthermore, to achieve tremendous achievements and new mindsets, the emerging technology BIM needs more education practices [78]. Universities must concentrate on the strategy of using BIM as a creative technology to encourage students to learn new skills and prepare them for their future activities in a more competitive environment [79]. However, very limited studies had been conducted on strategies implemented by educators to promote BIM education, which is not only beneficial for the AEC industry but also for BIM experts, developers and researchers [80,81]. Therefore, more proactive collaborations and BIM courses in civil engineering curriculum design are required to advance BIM educators and BIM talent development in the AEC industry [82,83].
(ii)
Automation Integrating Cluster (Yellow Color)
The automation cluster is integrated with construction, information technology, visualization and artificial intelligence. The concept of automation began in the last decade with the aim of reducing manpower and time [84]. The development of AI is speeding up rapidly, and the combination of AI with automation has started to change the AEC industry [85]. To achieve the new norm of quality and advancement in construction tasks, the AEC industry is adopting AI with an automation process [86]. Automation has implemented a framework for computers and devices and replaced the mechanism that was designed by combining the man with the computer [87]. In addition, ‘construction automation’ is a set of a new generation of technological innovations that will radically change the entire process and philosophy of construction [87]. Basically, executing the construction activities with the aid of robots is termed ‘construction automation’ [88]. Some researchers claim that ‘construction automation’ is the combination of computer-aided design and robot-based on-site techniques to accelerate overall activities [89]. Some of them, such as Willmann et al. [90], now use the term ‘digital manufacturing’ as a synonym for ‘construction automation’, especially when it comes to designing building projects. It is recommended that construction automation is actually occurring broadly nowadays; it is vitally important from the outset to build a standard practice to make clear the significant benefits and potential outcomes through data-based approaches and to establish a thorough understanding of construction automation [91].
(iii)
Photogrammetry Integrating Cluster (Pink Color)
The cluster of photogrammetry comprises (light detection and ranging) LiDAR, terrestrial laser scanning, buildings, damage, mapping, point clouds and inspection. In the last two decades, 3D building modeling became one of the most popular and hottest topics in photogrammetry, and it seems that photogrammetry is the only commercial way to acquire genuine 3D city data [92,93]. The majority of methods for 3D building modeling and LIDAR for building structures can be improved by oblique models so that the simulation of the facade or other manual reconstruction processes is only required for the user so that the operator can decide the boundaries to reflect the building model, and the process is not too time-consuming [94,95]. Due to terrestrial laser scanning results, the reconstruction of the facade has proven to be a valuable source in recent years. There can be up to 100 or 1000 points per square meter of steady laser scanning density in urban areas, which is high enough to record a lot of data on building facades. Various measures, such as image matching, intersection and resection, can be skipped compared to image models, whereas image interpretations are not needed for laser-based reconstruction approaches that are faced with major challenges such as extracting significant structures from large quantities of data [96]. Furthermore, a recent study conducted by L. Yang et al. [97] explored the semi-automated generation for steel structures based on terrestrial laser scanning data. It is recommended to expand the established technique to other popular types of project structures, such as L-shaped structures, T-shaped structures and other structures generated by the combination of simple primitives.
(iv)
Civil Engineering Integrating Cluster (Blue Color)
The civil engineering cluster is the combination of digital image correlation, displacement measurement, structural health monitoring, computer vision and unmanned aerial vehicle. Civil engineering infrastructure such as bridges, houses and tunnels continue to be used amid the ageing and corrosion of their construction life [98]. Conventional inspection and monitoring techniques can yield contradictory results, are labor-intensive and too time-consuming to be considered successful for large-scale monitoring [99]. New structural health monitoring systems must therefore be developed that are automated, highly accurate, minimally invasive and cost-effective [100,101]. Three-dimensional (3D) digital image correlation (DIC) systems are capable of extracting full-field strain, displacement, and geometry profiles [102]. Furthermore, when this measurement technique is introduced within the Unmanned Aerial Vehicle (UAV), the ability to accelerate the optical-based measurement process is improved as well as the downtime of the infrastructure is minimized [103]. These resulting credibility maps of the interest framework can be easily interpreted by qualified staff [104]. Moreover, a recent study conducted by Khuc et al. [105] attempted to enhance the displacement measurement methods by incorporating UAV and computer vision algorithms. Although this research has many advantages but also have some limitation such as UAV cameras, weather conditions, UAV crashes and turbulence. These limitations should be feasible for future works [105].
(v)
Augmented Reality (AR) and Computer Applications Integrating Cluster (Light Blue Color–Orange Color)
AR integrated cluster consists of the construction site and mobile computing and represented by light blue in color, while computer applications cluster consists of tracking and represented by orange color. The concept of using AR technology for building planning has gained popularity as desktop computers have supported more sophisticated graphics capabilities [106]. It has been found that AR is supposed to be as large a step as the transformation from 2D line drawings to photorealistic 3D projections [107]. In addition, the use of mobile computing in the building is becoming an important research subject in the field of construction information technology [108]. Although AR and computer applications play a tremendous role and boost up the DTs, they have some limitations [109,110]. It is suggested to integrate AR with other emerging digital technologies such as block chain, IoT (internet of things) and MR (mixed reality) to create a near-real-time virtual environment [111].
(vi)
Construction Management Integrating Cluster (Purple Color)
Construction management integrating cluster consists of construction education, construction technology and RFID. Various studies have been conducted on the influence of RFID on construction automated management [112,113,114]. Furthermore, the incorporation of RFID and BIM significantly enhances quality control, work logistics, construction safety and has become a hot topic in related studies [115,116,117]. However, there is a need to further integrate RFID with other emerging DTs in the AEC industry for better visualization in construction management activities. The key challenge of raising the productivity of RFID and its incorporation with other digital technologies (e.g., GIS and AR/VR) is the lack of interoperability [118,119,120] concerning data storage, storage, semantics and ontology [121,122]. For instance, Xie et al. [123] defined one of the key semantic interoperability gaps between RFID and VR, needing further future work to improve the interoperable framework.

4.4. Digital Technologies in the AEC Industry

Based on 200 papers, it was discovered that BIM is the most important digital technology (DT) in the AEC industry, followed by robotics, photogrammetry, laser scanning, images, AR, image processing techniques, RFID, VR and mobiles. As shown in Figure 6, BIM has a greater presence in the AEC industry than other DTs. BIM provides the possibility of simulating a construction project in a simulated environment. A building information model, or an accurate virtual model of a building, is digitally built using BIM technology. When finished, the building information model will contain detailed geometry and relevant data to support the design, procurement, manufacturing and construction activities necessary to realize the building. Although every DTs have its own credibility however various researchers used BIM integration with other DTs due to the more potential in the AEC industry. For instance, BIM integration with RFID [114,124,125,126], BIM integration with AR/VR technologies [127,128], BIM integration with IoT [129,130,131,132], BIM integration with GIS [65,133], BIM integration with laser scanning [134,135,136] and BIM integration with UAV technology [10,137]. Robotic technology has also been found to be the second most emerging technology after BIM, as shown in Figure 6. Robotic technology has the potential to reduce labor costs and improve quality and productivity [138]. Furthermore, robotic technology can reduce damage and free employees from hazardous tasks [138]. Bock [87] argues that traditional construction approaches have reached their limits and that robotics and automation technologies are capable of addressing the challenges of efficiency in the AEC industry. The history of robotic technology was developed in the 1960s, but the level of adoption of robotic technology in the AEC industry is very slow [139]. Several studies have highlighted the factors leading to the lower adoption of robotic technology. For example, a detailed study was presented on the challenges of robotics development in Japan, Malaysia and Australia [140]. Although there is considerable potential for robotic technology in the AEC industry, the challenges in the adoption of robotic technology have not been fully documented. It is, therefore, recommended that there be a clear, up-to-date study and further discussion of limiting adaptation factors [141].

4.5. Systematic Analysis

The systematic analysis of selected papers was provided in this section in order to provide an in-depth insight into research on DTs in the AEC industry. Among the 1778 publications resulting from bibliometric analysis, a screening procedure was performed to remove duplicate articles, irrelevant papers, and non-peer-reviewed papers. In the end, 200 articles were chosen following the screening process. Based on the subsequent papers, new advancements, challenges, negative attitudes and future directions have been explored.

4.5.1. New Advancements towards DTs in the AEC Industry

The phenomenon of DTs has been emerging since the 1930s, and with the passing of time, new technologies, possibilities and growth have been seen in the AEC industry [142]. Compared to the 20th century, the 21st century is well ahead in terms of technology, innovation and digitalization, and is also on the rise [55,143]. For example, BIM is the latest and emerging DT of the 21st century; no doubt the BIM idea was developed in the 20th century [144], but BIM reached its height in the 21st century [145,146]. Thus, BIM is a growing area of study and practice that integrates the diverse information domains of the AEC industry [147]. That is why other DTs have been integrated with BIM, such as BIM–RFID [124,125], BIM–GIS [148], BIM–AR/VR [149,150] and BIM–UAV [137,151] and so on. In addition, BIM was presented as a major technological improvement on conventional CAD, providing more intelligence and interoperability capabilities [152]. It was found that there several studies related to BIM integrates with sustainability and green house buildings [153,154,155,156,157,158] in developed countries. A recent study conducted by Naji et al. [159] explored the BIM in lower consumption electric power but was limited to questionnaire data and simulation work and provide a platform for future researchers to explore this simulation work in real-time case studies for validation purposes. Now, it is time to explore more energy consumption techniques, new carbon emission techniques integrating with BIM and other emerging technologies in developing countries [160].
Furthermore, it was found that very few studies have been conducted on BIM integration with block chains [161]. Hence, there is a need for time to explore more studies on block chain. Block chain is basically a growing emerging technology that has received significant momentum in various industries in both the public and private sectors in recent years [162]. With the advancement of block chain technology, the direction has gained new momentum in the smart contract technologies such as chain codes, automated code-checking compliances and BIM–cyber security to provide a platform for transparency and security [163]. Therefore, it is recommended to explore more research in the field of block chain and cyber security incorporated with BIM will gain a new boost in the AEC industry. Table 4 elaborates the new advancements towards DTs in the AEC industry.
In addition, the integration of DTs with safety management was explored in previous studies [184,185]. In developed countries such as Spain, Germany and the United Kingdom, BIM has already been introduced in the area of construction safety management [186]. Future research in developing countries with the incorporation of BIM with other DTs in the field of safety management needs to be discussed [117].
Last but not least, mobile technology leads towards DTs in the AEC industry in a bright way. With the development of 5G technology, there is a boost in the field of DTs [187]. A recent study conducted by Chew et al. [188] explored the roadmap of 5G technology for smart buildings. However, this work has various contributions in the AEC industry but is limited to building structures. This work can be extended to other infrastructures as well. In addition, a recent survey of 6G exploration was conducted in China [189]. With the advancement of 6G technology, the AEC industry will be improved, and thus, the DTs will have a significant effect on the growth and prosperity prospects of the AEC industry.

4.5.2. Challenges towards DTs in the AEC Industry

In this section, several challenges have been explored for the adoption of DTs in the AEC industry. However, it was concluded that more than 80% of the cost of using technology is estimated to occur after the initial purchase of the technology [190]. Purchasing a VR and AR headset with complete features for construction use could cost as little as $500 [191]. However, support systems cost, such as game engine software, and hardware components cost, such as laptop, phone and motion tracker, could easily surpass $5000. In addition, to build an interactive testing environment for the significant use of on-site DTs, the developer should be employed, and this requires huge costs [191]. The same applies to the use of WSDs, and these devices are cheap to acquire but very costly to use and maintain for a specific period of time. It was found that IoT-supported wearable devices could cost $100 per clip-on unit, with an additional networking cost of $12,000–24,000 per year [192]. Besides costs, there are various challenges that are well documented in Table 5.
It has been found that numerous challenges are on the way to reducing the adoption of DTs in the AEC industry. However, several researchers have conducted a study to find a solution and alleviate the challenges of implementing DTs in the AEC industry, but more work still needs to be conducted. Future work needs to be conducted on strategies to reduce challenges in order to smoothly implement DTs in the AEC industry.

4.5.3. Negative Attitudes towards DTs in the AEC Industry

In this section, negative attitude towards DTs in the AEC industry has been explored. Corruption is the most dominant factor leading the AEC industry to decline [214]. The negativity of corruption exists at every point of construction projects, including numerous project stakeholders, government officials, engineers, suppliers and so on [215]. For a significant time, the AEC industry had no concerted strategy to tackle the issue, but a series of events occurred in the late 1990s that contributed to the industry’s most far-reaching attempts to counter corruption. Global governments, engineering/construction organizations and individuals are making efforts to eradicate corruption and conduct business in an honest, transparent and equitable manner [215]. In addition, a range of other negative attitudes towards DTs in the AEC industry include kickbacks, bribery, tender rigging, fraud, unethical practices and conflicts of interest [216]. Furthermore, kickbacks and bribery are considered as the two faces of the same illegal coin. However, tender rigging is another crucial problem that can take a number of forms. Owners’ employees can participate by setting a very short period of bidding so that only companies they have unlawfully informed of the forthcoming offer have adequate time to prepare a sound bid. Owners’ workers can also exempt interested firms from the bid list and only allow “favored” firms to participate [217]. Likewise, fraud, unethical practices and conflict of interest also produce negativity on the way of development and productivity. Fraud is an economic crime involving activities such as swindling, trickery, misinformation or deceit [218]. Hence, it can be said that these negative factors not only harm the image of the AEC industry but also pose serious questions about DTs. Table 6 illustrates a better picture of negative attitudes towards DTs in the AEC industry.

4.5.4. Future Directions towards DTs in the AEC Industry

Robotics is considered to be one of the primary fields for DTs in the AEC industry, but little attention has been paid to robotic systems. Performing various construction tasks is dangerous and can lead to serious injuries and deaths, such as construction works above the sea, at heights and inside deep trenches; thus, there is a need to develop user-friendly, advanced and smart robots in future. The majority of the recent studies focused on the use of AI methods to define, assess, monitor and handle security threats instead of replacing people with robots in risky conditions. It is really important to concentrate future research on inventing robots that function without human interference to solve that problem [228]. In addition, it is suggested that collaborative robots be created and used, i.e., robots designed to work with humans as employees add value to projects instead of replacing people fully with robots. Robotics would add immense value to the AEC industry, such as improved efficiency, performance, safety and quality. Likewise, in the AEC industry, modularization and pre-fabrication, along with 3D printing, these advantages are now becoming a prevalent approach. Another interesting area of science, such as the development and implementation of robots for building construction in factories, is the integration of robotics with modular construction technology and 3D printing technology. The key question here is how robots can affect employee emotions and performance. This should be addressed in future studies. It is also recommended to integrate BIM with cyber security to ensure the potential of security of databases.

5. Summary of Findings

Figure 7 provides a good picture of the overview of results in DTs in the AEC industry under one umbrella. It is clear that the United States has the leading research in the field of DTs led by China, Spain, the United Kingdom and Australia. The main topics of the author’s keyword co-occurrence are also related to BIM, construction management, civil engineering, photogrammetry, construction, AR, information technology and automation. In addition, BIM is considered to be the most emerging DTs among all other DTs in the AEC industry. BIM–block chain integration is seen as the most exciting new technology in the AEC industry in terms of DTs. In addition, transparency and security issues need to be further discussed in the future. In addition, inadequate skills, government policies, barriers to culture and costs are the most important challenges facing the AEC industry. There is a need to include the correct direction to be taken in order to escape these challenges. In addition, laws and regulations in developed countries should be in place to reduce negative attitudes and further boost the constructive approach of the AEC industry.

6. Conclusions

This study focuses on the current state-of-the-art research related to DTs in the AEC industry. A mixed systematic review method was adopted in order to provide a mapping of DTs and deeper insights into the research gaps and needs. It was found that the pattern of DTs publications in the AEC industry in the 21st century is strongly comparable to the 20th century. Likewise, the majority of publications in DTs in the AEC industry have also contributed by Automation in construction. Furthermore, the United States is the leading source of DTs research in the AEC industry. It was also found that BIM in the AEC industry is considered to be the emerging DTs in all other DTs in the AEC industry. From a theoretical perspective, this research is unique in the following ways: (a) this study presents the first bibliometric-qualitative literature review appraising the state-of-the-art of research on DTs in the AEC industry, (b) the research focuses on the research of DTs in the AEC context, including new advancements, challenges, negative attitudes, and future directions (c) and offering recommendations that provide guidelines on how to address the shortcomings in defining further research. From a practical perspective, this study can help practitioners with a modulated reference point that is easily accessible and grasp the latest techniques and methods of DTs research in the AEC industry.
Despite its contributions, this study has limitations. First, the data is extracted from a Scopus database. Further data will, in future, be collected by integrating data from different databases for quantitative and qualitative analyses (e.g., Google Scholar, Web of Science and so on). Secondly, this research was limited to journal articles only. For these reasons, the results of the research do not fully reflect the entire available literature on DTs in the AEC industry. The limitations listed above provide excellent opportunities for further study, though they should be taken into account when evaluating the results of the research. In future studies, however, data from various sources and a set of parameters may be used for literary impact evaluations, coherence, and linkages to overcome these limitations.

Author Contributions

B.M.: Conceptualization, Investigation, Data creation, Writing—original draft. I.O.: Supervision, Writing—review and editing. J.C.P.: Writing—review and editing. 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.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, M.; Wang, C.C.; Sepasgozar, S.; Zlatanova, S. A Systematic Review of Digital Technology Adoption in Off-Site Construction: Current Status and Future Direction towards Industry 4.0. Buildings 2020, 10, 204. [Google Scholar] [CrossRef]
  2. Patel, S.; Patt, Y. Introduction to Computing Systems: From Bits & Gates to C & Beyond; McGraw-Hill Professional: New York, NY, USA, 2019. [Google Scholar]
  3. Xue, X.; Shen, Q.; Ren, Z. Critical Review of Collaborative Working in Construction Projects: Business Environment and Human Behaviors. J. Manag. Eng. 2010, 26, 196–208. [Google Scholar] [CrossRef]
  4. Alaloul, W.S.; Liew, M.; Zawawi, N.A.W.A.; Kennedy, I.B. Industrial Revolution 4.0 in the construction industry: Challenges and opportunities for stakeholders. Ain Shams Eng. J. 2020, 11, 225–230. [Google Scholar] [CrossRef]
  5. Maskuriy, R.; Selamat, A.; Maresova, P.; Krejcar, O.; David, O.O. Industry 4.0 for the construction industry: Review of man-agement perspective. Economies 2019, 7, 68. [Google Scholar] [CrossRef] [Green Version]
  6. Woodhead, R.; Stephenson, P.; Morrey, D. Digital construction: From point solutions to IoT ecosystem. Autom. Constr. 2018, 93, 35–46. [Google Scholar] [CrossRef] [Green Version]
  7. Ozturk, G.B. Interoperability in building information modeling for AECO/FM industry. Autom. Constr. 2020, 113, 103122. [Google Scholar] [CrossRef]
  8. Kim, K.; Kim, H.; Kim, H. Image-based construction hazard avoidance system using augmented reality in wearable device. Autom. Constr. 2017, 83, 390–403. [Google Scholar] [CrossRef]
  9. Sacks, R.; Perlman, A.; Barak, R. Construction safety training using immersive virtual reality. Constr. Manag. Econ. 2013, 31, 1005–1017. [Google Scholar] [CrossRef]
  10. Rizo-Maestre, C.; González-Avilés, Á.; Galiano-Garrigós, A.; Andújar-Montoya, M.D.; Puchol-García, J.A. UAV BIM: Incor-poration of Photogrammetric Techniques in Architectural Projects with Building Information Modeling versus Classical Work Processes+. Remote Sens. 2020, 12, 2329. [Google Scholar] [CrossRef]
  11. Martínez-Rojas, M.; Marín, N.; Vila, M.A. The Role of Information Technologies to Address Data Handling in Construction Project Management. J. Comput. Civ. Eng. 2016, 30, 04015064. [Google Scholar] [CrossRef]
  12. Song, Y.; Wang, X.; Tan, Y.; Wu, P.; Sutrisna, M.; Cheng, J.C.P.; Hampson, K. Trends and Opportunities of BIM-GIS Integration in the Architecture, Engineering and Construction Industry: A Review from a Spatio-Temporal Statistical Perspective. ISPRS Int. J. Geo-Inf. 2017, 6, 397. [Google Scholar] [CrossRef] [Green Version]
  13. Ergen, E.; Akinci, B.; Sacks, R. Tracking and locating components in a precast storage yard utilizing radio frequency identi-fication technology and GPS. Autom. Constr. 2007, 16, 354–367. [Google Scholar] [CrossRef]
  14. Mneymneh, B.E.; Abbas, M.; Khoury, H. Vision-based framework for intelligent monitoring of hardhat wearing on con-struction sites. J. Comput. Civ. Eng. 2019, 33, 4018066. [Google Scholar] [CrossRef]
  15. Alizadehsalehi, S.; Hadavi, A.; Huang, J.C. From BIM to extended reality in AEC industry. Autom. Constr. 2020, 116, 103254. [Google Scholar] [CrossRef]
  16. Darko, A.; Chan, A.P.; Adabre, M.A.; Edwards, D.J.; Hosseini, M.R.; Ameyaw, E.E. Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Autom. Constr. 2020, 112, 103081. [Google Scholar] [CrossRef]
  17. Davtalab, O.; Kazemian, A.; Khoshnevis, B. Perspectives on a BIM-integrated software platform for robotic construction through Contour Crafting. Autom. Constr. 2018, 89, 13–23. [Google Scholar] [CrossRef]
  18. Elghaish, F.; Abrishami, S.; Hosseini, M.R. Integrated project delivery with blockchain: An automated financial system. Autom. Constr. 2020, 114, 103182. [Google Scholar] [CrossRef]
  19. Zaher, M.; Greenwood, D.; Marzouk, M. Mobile augmented reality applications for construction projects. Constr. Innov. 2018, 18, 152–166. [Google Scholar] [CrossRef] [Green Version]
  20. Dinis, F.M.; Sanhudo, L.; Martins, J.P.; Ramos, N.M.M. Improving project communication in the architecture, engineering and construction industry: Coupling virtual reality and laser scanning. J. Build. Eng. 2020, 30, 101287. [Google Scholar] [CrossRef]
  21. Noghabaei, M.; Heydarian, A.; Balali, V.; Han, K. Trend Analysis on Adoption of Virtual and Augmented Reality in the Architecture, Engineering, and Construction Industry. Data 2020, 5, 26. [Google Scholar] [CrossRef] [Green Version]
  22. Maffei, L.; Masullo, M.; Pascale, A.; Ruggiero, G.; Romero, V.P. Immersive virtual reality in community planning: Acoustic and visual congruence of simulated vs real world. Sustain. Cities Soc. 2016, 27, 338–345. [Google Scholar] [CrossRef]
  23. Iachini, T.; Maffei, L.; Ruotolo, F.; Senese, V.P.; Ruggiero, G.; Masullo, M.; Alekseeva, N. Multisensory Assessment of Acoustic Comfort Aboard Metros: A Virtual Reality Study. Appl. Cogn. Psychol. 2012, 26, 757–767. [Google Scholar] [CrossRef]
  24. Vincke, S.; Hernandez, R.D.L.; Bassier, M.; Vergauwen, M. Immersive Visualisation of Construction Site Point Cloud Data, Meshes and BIM Models in a VR Environment using a Gaming Engine. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2019, XLII-5/W2, 77–83. [Google Scholar] [CrossRef] [Green Version]
  25. Azhar, S. Role of Visualization Technologies in Safety Planning and Management at Construction Jobsites. Procedia Eng. 2017, 171, 215–226. [Google Scholar] [CrossRef]
  26. Jensen, C.G. Collaboration and dialogue in Virtual reality. J. Probl. Based Learn. High. Educ. 2017, 5. [Google Scholar] [CrossRef]
  27. Pauwels, P.; Zhang, S.; Lee, Y.-C. Semantic web technologies in AEC industry: A literature overview. Autom. Constr. 2017, 73, 145–165. [Google Scholar] [CrossRef]
  28. Irizarry, J.; Karan, E.P.; Jalaei, F. Integrating BIM and GIS to improve the visual monitoring of construction supply chain management. Autom. Constr. 2013, 31, 241–254. [Google Scholar] [CrossRef]
  29. De Soto, B.G.; Agustí-Juan, I.; Joss, S.; Hunhevicz, J. Implications of Construction 4.0 to the workforce and organizational structures. Int. J. Constr. Manag. 2019, 1–13. [Google Scholar] [CrossRef]
  30. Zaki, T.; Khalil, C. QR-coded clash-free drawings: An integrated system of BIM and Augmented reality to improve con-struction project visualization. In Proceedings of the ICSC15—The Canadian Society for Civil Engineering’s 5th International/11th Construction Specialty Conference, Vancouver, BC, Canada, 7–10 June 2015. [Google Scholar]
  31. Patrício, D.I.; Rieder, R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput. Electron. Agric. 2018, 153, 69–81. [Google Scholar] [CrossRef] [Green Version]
  32. Mellit, A.; Kalogirou, S.A. Artificial intelligence techniques for photovoltaic applications: A review. Prog. Energy Combust. Sci. 2008, 34, 574–632. [Google Scholar] [CrossRef]
  33. Araszkiewicz, K. Digital Technologies in Facility Management—The state of Practice and Research Challenges. Procedia Eng. 2017, 196, 1034–1042. [Google Scholar] [CrossRef]
  34. Guo, B.H.; Scheepbouwer, E.; Yiu, T.W.; Gonzalez, V. Overview and Analysis of Digital Technologies Designed for Construction Safety Management. In Proceedings of the AUBEA 2017: Australasian Universities Building Education Association Conference, Melbourne, Australia, 3–5 July 2017. [Google Scholar]
  35. Ibem, E.O.; Laryea, S. Survey of digital technologies in procurement of construction projects. Autom. Constr. 2014, 46, 11–21. [Google Scholar] [CrossRef]
  36. Markoulli, M.; Lee, C.; Byington, E.; Felps, W.A. Mapping Human Resource Management: Reviewing the field and charting future directions. Hum. Resour. Manag. Rev. 2017, 27, 367–396. [Google Scholar] [CrossRef]
  37. Hamelink, C.J. New Information and Communication Technologies, Social Development and Cultural Change; United Nations Research Institute for Social Development Geneva: Geneva, Switzerland, 1997. [Google Scholar]
  38. Wu, L.X.; Yin, Z.R.; Zhong, Y.P. Restudy on digital mine: Characteristics, framework and key technologies. J. China Coal Soc. 2003, 28, 1–7. [Google Scholar]
  39. Saad, I.M.H.; Hancher, D.E. Multimedia for Construction Project Management: Project Navigator. J. Constr. Eng. Manag. 1998, 124, 82–89. [Google Scholar] [CrossRef]
  40. Matt, C.; Hess, T.; Benlian, A. Digital Transformation Strategies. Bus. Inf. Syst. Eng. 2015, 57, 339–343. [Google Scholar] [CrossRef]
  41. Taipale, S. Synchronicity matters: Defining the characteristics of digital generations. Inf. Commun. Soc. 2016, 19, 80–94. [Google Scholar] [CrossRef] [Green Version]
  42. Yoo, Y.; Lyytinen, K. Distributed Innovation in Classes of Networks. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), Waikoloa, HI, USA, 7–10 January 2008; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2008; p. 58. [Google Scholar]
  43. Lavikka, R.; Kallio, J.; Casey, T.; Airaksinen, M. Digital disruption of the AEC industry: Technology-oriented scenarios for possible future development paths. Constr. Manag. Econ. 2018, 36, 635–650. [Google Scholar] [CrossRef] [Green Version]
  44. Johnson, R.B.; Onwuegbuzie, A.J. Mixed Methods Research: A Research Paradigm Whose Time Has Come. Educ. Res. 2004, 33, 14–26. [Google Scholar] [CrossRef] [Green Version]
  45. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. Science mapping software tools: Review, analysis, and cooperative study among tools. J. Am. Soc. Inf. Sci. Technol. 2011, 62, 1382–1402. [Google Scholar] [CrossRef]
  46. Van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [Green Version]
  47. Booth, A.; Sutton, A.; Papaioannou, D. Systematic Approaches to a Successful Literature Review; SAGE: New York, NY, USA, 2016. [Google Scholar]
  48. Zhao, X.; Zuo, J.; Wu, G.; Huang, C. A bibliometric review of green building research 2000–2016. Arch. Sci. Rev. 2018, 62, 74–88. [Google Scholar] [CrossRef]
  49. Meho, L.I.; Rogers, Y. Citation counting, citation ranking, and h-index of human-computer interaction researchers: A com-parison of Scopus and Web of Science. J. Am. Soc. Inf. Sci. Technol. 2008, 59, 1711–1726. [Google Scholar] [CrossRef] [Green Version]
  50. Yin, X.; Liu, H.; Chen, Y.; Al-Hussein, M. Building information modelling for off-site construction: Review and future direc-tions. Autom. Constr. 2019, 101, 72–91. [Google Scholar] [CrossRef]
  51. Van Eck, N.J.; Waltman, L. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics 2017, 111, 1053–1070. [Google Scholar] [CrossRef] [Green Version]
  52. Vilutiene, T.; Kalibatiene, D.; Hosseini, M.R.; Pellicer, E.; Zavadskas, E.K. Building Information Modeling (BIM) for Structural Engineering: A Bibliometric Analysis of the Literature. Adv. Civ. Eng. 2019, 2019, 1–19. [Google Scholar] [CrossRef]
  53. Santos, R.; Costa, A.A.; Grilo, A. Bibliometric analysis and review of Building Information Modelling literature published between 2005 and 2015. Autom. Constr. 2017, 80, 118–136. [Google Scholar] [CrossRef]
  54. Babalola, A.; Musa, S.; Akinlolu, M.T.; Haupt, T.C. A bibliometric review of advances in building information modeling (BIM) research. J. Eng. Des. Technol. 2021. [Google Scholar] [CrossRef]
  55. Robin, B.R. Digital Storytelling: A Powerful Technology Tool for the 21st Century Classroom. Theory Pract. 2008, 47, 220–228. [Google Scholar] [CrossRef]
  56. Bilal, M.; Oyedele, L.O.; Qadir, J.; Munir, K.; Ajayi, S.O.; Akinade, O.O.; Owolabi, H.A.; Alaka, H.A.; Pasha, M. Big Data in the construction industry: A review of present status, opportunities, and future trends. Adv. Eng. Inform. 2016, 30, 500–521. [Google Scholar] [CrossRef]
  57. Council, N.R. Funding a Revolution: Government Support for Computing Research; National Academies Press: Washington, DC, USA, 1999. [Google Scholar]
  58. Gu, N.; London, K. Understanding and facilitating BIM adoption in the AEC industry. Autom. Constr. 2010, 19, 988–999. [Google Scholar] [CrossRef]
  59. Serenko, A. The development of an AI journal ranking based on the revealed preference approach. J. Inf. 2010, 4, 447–459. [Google Scholar] [CrossRef]
  60. Perianes-Rodriguez, A.; Waltman, L.; van Eck, N.J. Constructing bibliometric networks: A comparison between full and fractional counting. J. Inf. 2016, 10, 1178–1195. [Google Scholar] [CrossRef] [Green Version]
  61. Ganbat, T.; Chong, H.-Y.; Liao, P.-C.; Wu, Y.-D. A Bibliometric Review on Risk Management and Building Information Modeling for International Construction. Adv. Civ. Eng. 2018, 2018, 1–13. [Google Scholar] [CrossRef] [Green Version]
  62. Chihib, M.; Salmerón-Manzano, E.; Novas, N.; Manzano-Agugliaro, F. Bibliometric Maps of BIM and BIM in Universities: A Comparative Analysis. Sustainability 2019, 11, 4398. [Google Scholar] [CrossRef] [Green Version]
  63. Saka, A.B.; Chan, D.W.M. A Scientometric Review and Metasynthesis of Building Information Modelling (BIM) Research in Africa. Buildings 2019, 9, 85. [Google Scholar] [CrossRef] [Green Version]
  64. van Eck, N.J.; Waltman, L. CitNetExplorer: A new software tool for analyzing and visualizing citation networks. J. Inf. 2014, 8, 802–823. [Google Scholar] [CrossRef] [Green Version]
  65. Wang, H.; Pan, Y.; Luo, X. Integration of BIM and GIS in sustainable built environment: A review and bibliometric analysis. Autom. Constr. 2019, 103, 41–52. [Google Scholar] [CrossRef]
  66. Olawumi, T.O.; Chan, D.W.M.; Wong, J.K.W. Evolution in the intellectual structure of BIM research: A bibliometric analysis. J. Civ. Eng. Manag. 2017, 23, 1060–1081. [Google Scholar] [CrossRef]
  67. Li, X.; Wu, P.; Shen, Q.; Wang, X.; Teng, Y. Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach. Autom. Constr. 2017, 84, 195–206. [Google Scholar] [CrossRef]
  68. Zheng, L.; Chen, K.; Lu, W. Bibliometric Analysis of Construction Education Research from 1982 to 2017. J. Prof. Issues Eng. Educ. Pract. 2019, 145, 04019005. [Google Scholar] [CrossRef]
  69. Jin, R.; Zou, Y.; Gidado, K.; Ashton, P.; Painting, N. Scientometric analysis of BIM-based research in construction engineering and management. Eng. Constr. Arch. Manag. 2019, 26, 1750–1776. [Google Scholar] [CrossRef] [Green Version]
  70. Manzoor, B.; Othman, I. Safety Management Model During Construction Focusing on Building Information Modeling (BIM). In Advances in Civil Engineering Materials: Selected Articles from the International Conference on Architecture and Civil Engineering (ICACE2020); Springer Nature: Singapore, 2021; p. 31. [Google Scholar] [CrossRef]
  71. Jin, R.; Zhong, B.; Ma, L.; Hashemi, A.; Ding, L. Integrating BIM with building performance analysis in project life-cycle. Autom. Constr. 2019, 106, 102861. [Google Scholar] [CrossRef]
  72. Van Eck, N.J.; Waltman, L. VOSviewer Manual; Univeristeit Leiden: Leiden, The Netherlands, 2013. [Google Scholar]
  73. Liu, H.; Singh, G.; Lu, M.; Bouferguene, A.; Al-Hussein, M. BIM-based automated design and planning for boarding of light-frame residential buildings. Autom. Constr. 2018, 89, 235–249. [Google Scholar] [CrossRef]
  74. Choo, S.; Park, H.; Kim, T.; Seo, J. Analysis of Trends in Korean BIM Research and Technologies Using Text Mining. Appl. Sci. 2019, 9, 4424. [Google Scholar] [CrossRef] [Green Version]
  75. Cheng, J.C.P.; Deng, Y.; Anumba, C. Mapping BIM schema and 3D GIS schema semi-automatically utilizing linguistic and text mining techniques. J. Inf. Technol. Constr. 2015, 20, 193–212. [Google Scholar]
  76. Yarmohammadi, S.; Pourabolghasem, R.; Castro-Lacouture, D. Mining implicit 3D modeling patterns from unstructured temporal BIM log text data. Autom. Constr. 2017, 81, 17–24. [Google Scholar] [CrossRef]
  77. Pan, Y.; Zhang, L. BIM log mining: Learning and predicting design commands. Autom. Constr. 2020, 112, 103107. [Google Scholar] [CrossRef]
  78. Suwal, S.; Jäväjä, P.; Salin, J. BIM Education: Implementing and Reviewing “OpeBIM”—BIM for Teachers. In Computing in Civil and Building Engineering; ASCE: Reston, VA, USA, 2014; pp. 2151–2158. [Google Scholar]
  79. Bozoglu, J. Collaboration and coordination learning modules for BIM education. J. Inf. Technol. Constr. 2016, 21, 152–163. [Google Scholar]
  80. Abdirad, H.; Dossick, C.S. BIM curriculum design in architecture, engineering, and construction education: A systematic review. J. Inf. Technol. Constr. 2016, 21, 250–271. [Google Scholar]
  81. Abbas, A.; Din, Z.U.; Farooqui, R. Integration of BIM in Construction Management Education: An Overview of Pakistani Engineering Universities. Procedia Eng. 2016, 145, 151–157. [Google Scholar] [CrossRef] [Green Version]
  82. Wu, W.; Issa, R.R.A. BIM education and recruiting: Survey-based comparative analysis of issues, perceptions, and collabo-ration opportunities. J. Prof. Issues Eng. Educ. Pract. 2014, 140, 4013014. [Google Scholar] [CrossRef]
  83. Qureshi, A.H.; Alaloul, W.S.; Manzoor, B.; Musarat, M.A.; Saad, S. Implications of Machine Learning Integrated Technologies for Construction Progress Detection Under Industry 4.0 (IR 4.0). In Proceedings of the 2020 Second International Sustainability and Resilience Conference, Sakheer, Bahrain, 11–12 November 2020; pp. 1–6. [Google Scholar]
  84. Wang, W.; Siau, K. Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. J. Database Manag. 2019, 30, 61–79. [Google Scholar] [CrossRef]
  85. Shekhar, S.S. Artificial Intelligence in Automation. Artif. Intell. 2019, 3085, 14–17. [Google Scholar]
  86. Donepudi, P.K. Application of Artificial Intelligence in Automation Industry. Asian J. Appl. Sci. Eng. 2018, 7, 7–20. [Google Scholar]
  87. Bock, T. The future of construction automation: Technological disruption and the upcoming ubiquity of robotics. Autom. Constr. 2015, 59, 113–121. [Google Scholar] [CrossRef]
  88. Skibniewski, M.J.; Russell, J.S. Construction Robot Fleet Management System Prototype. J. Comput. Civ. Eng. 1991, 5, 444–463. [Google Scholar] [CrossRef]
  89. Keating, S.; Oxman, N. Compound fabrication: A multi-functional robotic platform for digital design and fabrication. Robot. Comput. Manuf. 2013, 29, 439–448. [Google Scholar] [CrossRef]
  90. Willmann, J.; Knauss, M.; Bonwetsch, T.; Apolinarska, A.A.; Gramazio, F.; Kohler, M. Robotic timber construction—Expanding additive fabrication to new dimensions. Autom. Constr. 2016, 61, 16–23. [Google Scholar] [CrossRef]
  91. Chen, Q.; de Soto, B.G.; Adey, B.T. Construction automation: Research areas, industry concerns and suggestions for ad-vancement. Autom. Constr. 2018, 94, 22–38. [Google Scholar] [CrossRef]
  92. Mao, B.; Ban, Y.; Harrie, L. A multiple representation data structure for dynamic visualisation of generalised 3D city models. ISPRS J. Photogramm. Remote Sens. 2011, 66, 198–208. [Google Scholar] [CrossRef]
  93. Julin, A.; Jaalama, K.; Virtanen, J.-P.; Pouke, M.; Ylipulli, J.; Vaaja, M.; Hyyppä, J.; Hyyppä, H. Characterizing 3D City Modeling Projects: Towards a Harmonized Interoperable System. ISPRS Int. J. Geo-Inf. 2018, 7, 55. [Google Scholar] [CrossRef] [Green Version]
  94. Cho, H.-B.; Cho, W.-S.; Park, J.-K.; Song, N.-H. 3D building modeling using aerial LiDAR data, Korean. J. Remote Sens. 2008, 24, 141–152. [Google Scholar]
  95. Zhang, Y.; Zhang, Z.; Zhang, J.; Wu, J. 3D Building Modelling with Digital Map, Lidar Data and Video Image Sequences. Photogramm. Rec. 2005, 20, 285–302. [Google Scholar] [CrossRef]
  96. Kersten, T.P.; Büyüksalİh, G.; Baz, İ.; Jacobsen, K. Documentation of Istanbul historic peninsula by kinematic terrestrial laser scanning. Photogramm. Rec. 2009, 24, 122–138. [Google Scholar] [CrossRef]
  97. Yang, L.; Cheng, J.C.; Wang, Q. Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data. Autom. Constr. 2020, 112, 103037. [Google Scholar] [CrossRef]
  98. Stajano, F.; Hoult, N.; Wassell, I.; Bennett, P.; Middleton, C.; Soga, K. Smart bridges, smart tunnels: Transforming wireless sensor networks from research prototypes into robust engineering infrastructure. Ad Hoc Netw. 2010, 8, 872–888. [Google Scholar] [CrossRef] [Green Version]
  99. Pour Rahimian, F.; Seyedzadeh, S.; Oliver, S.; Rodriguez, S.; Dawood, N. On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Autom. Constr. 2020, 110, 103012. [Google Scholar] [CrossRef]
  100. Ko, J.; Ni, Y. Technology developments in structural health monitoring of large-scale bridges. Eng. Struct. 2005, 27, 1715–1725. [Google Scholar] [CrossRef]
  101. Ostachowicz, W.; Güemes, A. New Trends in Structural Health Monitoring; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
  102. Aggelis, D.; Verbruggen, S.; Tsangouri, E.; Tysmans, T.; Van Hemelrijck, D. Characterization of mechanical performance of concrete beams with external reinforcement by acoustic emission and digital image correlation. Constr. Build. Mater. 2013, 47, 1037–1045. [Google Scholar] [CrossRef]
  103. Siebert, S.; Teizer, J. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Autom. Constr. 2014, 41, 1–14. [Google Scholar] [CrossRef]
  104. Wen, M.-C.; Kang, S.-C. Augmented Reality and Unmanned Aerial Vehicle Assist in Construction Management. Comput. Civil Build. Eng. 2014, 1570–1577. [Google Scholar] [CrossRef] [Green Version]
  105. Khuc, T.; Nguyen, T.A.; Dao, H.; Catbas, F.N. Swaying Displacement Measurement for Structural Monitoring Using Computer Vision and an Unmanned Aerial Vehicle. Measurement 2020, 159, 107769. [Google Scholar] [CrossRef]
  106. Li, X.; Yi, W.; Chi, H.-L.; Wang, X.; Chan, A.P. A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Autom. Constr. 2018, 86, 150–162. [Google Scholar] [CrossRef]
  107. Lin, K.-Y.; Son, J.W.; Rojas, E.M. A pilot study of a 3D game environment for construction safety education. J. Inf. Technol. Constr. 2011, 16, 69–84. [Google Scholar]
  108. Chen, Y.; Kamara, J.M. A framework for using mobile computing for information management on construction sites. Autom. Constr. 2011, 20, 776–788. [Google Scholar] [CrossRef]
  109. Nnaji, C.; Karakhan, A.A. Technologies for safety and health management in construction: Current use, implementation benefits and limitations, and adoption barriers. J. Build. Eng. 2020, 29, 101212. [Google Scholar] [CrossRef]
  110. Delgado, J.M.D.; Oyedele, L.; Beach, T.; Demian, P. Augmented and Virtual Reality in Construction: Drivers and Limitations for Industry Adoption. J. Constr. Eng. Manag. 2020, 146, 04020079. [Google Scholar] [CrossRef]
  111. Noghabaei, M.; Asadi, K.; Han, K. Virtual Manipulation in an Immersive Virtual Environment: Simulation of Virtual Assembly. Comput. Civ. Eng. 2019, 2019, 95–102. [Google Scholar] [CrossRef]
  112. Sardroud, J.M. Influence of RFID technology on automated management of construction materials and components. Sci. Iran. 2012, 19, 381–392. [Google Scholar] [CrossRef] [Green Version]
  113. Lu, W.; Huang, G.Q.; Li, H. Scenarios for applying RFID technology in construction project management. Autom. Constr. 2011, 20, 101–106. [Google Scholar] [CrossRef] [Green Version]
  114. Fang, Y.; Cho, Y.K.; Zhang, S.; Perez, E. Case study of BIM and cloud–enabled real-time RFID indoor localization for con-struction management applications. J. Constr. Eng. Manag. 2016, 142, 5016003. [Google Scholar] [CrossRef]
  115. Ikonen, J.; Knutas, A.; Hämäläinen, H.; Ihonen, M.; Porras, J.; Kallonen, T. Use of embedded RFID tags in concrete element supply chains. J. Inf. Technol. Constr. 2013, 18, 119–147. [Google Scholar]
  116. Costin, A.M.; Teizer, J.; Schoner, B. RFID and BIM-enabled worker location tracking to support real-time building protocol and data visualization. J. Inf. Technol. Constr. 2015, 20, 495–517. [Google Scholar]
  117. Manzoor, B.; Othman, I.; Manzoor, M. Evaluating the critical safety factors causing accidents in high-rise building projects. Ain Shams Eng. J. 2021. [Google Scholar] [CrossRef]
  118. Gökçe, K.U.; Gökçe, H.U.; Katranuschkov, P. IFC-Based Product Catalog Formalization for Software Interoperability in the Construction Management Domain. J. Comput. Civ. Eng. 2013, 27, 36–50. [Google Scholar] [CrossRef]
  119. Ciribini, A.; Ventura, S.M.; Paneroni, M. Implementation of an interoperable process to optimise design and construction phases of a residential building: A BIM Pilot Project. Autom. Constr. 2016, 71, 62–73. [Google Scholar] [CrossRef]
  120. Alizadehsalehi, S.; Hadavi, A.; Huang, J.C. Assessment of AEC Students’ Performance Using BIM-into-VR. Appl. Sci. 2021, 11, 3225. [Google Scholar] [CrossRef]
  121. Lee, S.-K.; Kim, K.; Yu, J. BIM and ontology-based approach for building cost estimation. Autom. Constr. 2014, 41, 96–105. [Google Scholar] [CrossRef]
  122. Nepal, M.; Staub-French, S. Supporting knowledge-intensive construction management tasks in BIM. J. Inf. Technol. Constr. 2016, 21, 13–38. [Google Scholar]
  123. Xie, H.; Shi, W.; Issa, R.R.A. Using rfid and real-time virtual reality simulation for optimization in steel construction. J. Inf. Technol. Constr. 2011, 16, 291–308. [Google Scholar]
  124. Meadati, P.; Irizarry, J.; Akhnoukh, A.K. BIM and RFID integration: A pilot study. Adv. Integr. Constr. Educ. Res. Pract. 2010, 5, 570–578. [Google Scholar]
  125. Li, C.Z.; Zhong, R.Y.; Xue, F.; Xu, G.; Chen, K.; Huang, G.G.; Shen, Q. Integrating RFID and BIM technologies for mitigating risks and improving schedule performance of prefabricated house construction. J. Clean. Prod. 2017, 165, 1048–1062. [Google Scholar] [CrossRef]
  126. Costin, A.M.; Teizer, J. Fusing passive RFID and BIM for increased accuracy in indoor localization. Vis. Eng. 2015, 3, 17. [Google Scholar] [CrossRef] [Green Version]
  127. Chen, K.; Chen, W.; Cheng, J.C.P.; Wang, Q. Developing Efficient Mechanisms for BIM-to-AR/VR Data Transfer. J. Comput. Civ. Eng. 2020, 34, 04020037. [Google Scholar] [CrossRef]
  128. Lin, Y.-C.; Chen, Y.-P.; Yien, H.-W.; Huang, C.-Y.; Su, Y.-C. Integrated BIM, game engine and VR technologies for healthcare design: A case study in cancer hospital. Adv. Eng. Inform. 2018, 36, 130–145. [Google Scholar] [CrossRef]
  129. Tang, S.; Shelden, D.R.; Eastman, C.M.; Pishdad-Bozorgi, P.; Gao, X. A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Autom. Constr. 2019, 101, 127–139. [Google Scholar] [CrossRef]
  130. Li, C.Z.; Xue, F.; Li, X.; Hong, J.; Shen, G.Q. An Internet of Things-enabled BIM platform for on-site assembly services in prefabricated construction. Autom. Constr. 2018, 89, 146–161. [Google Scholar] [CrossRef]
  131. Zhai, Y.; Chen, K.; Zhou, J.X.; Cao, J.; Lyu, Z.; Jin, X.; Shen, G.Q.; Lu, W.; Huang, G.Q. An Internet of Things-enabled BIM platform for modular integrated construction: A case study in Hong Kong. Adv. Eng. Inform. 2019, 42, 100997. [Google Scholar] [CrossRef]
  132. Dave, B.; Buda, A.; Nurminen, A.; Främling, K. A framework for integrating BIM and IoT through open standards. Autom. Constr. 2018, 95, 35–45. [Google Scholar] [CrossRef]
  133. Sani, M.J.; Rahman, A.A. GIS and BIM integration at data level: A review. ISPRS—Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, XLII-4/W9, 299–306. [Google Scholar] [CrossRef] [Green Version]
  134. Sanhudo, L.; Ramos, N.M.M.; Martins, J.P.; Almeida, R.; Barreira, E.; Simões, M.L.; Cardoso, V.E.M. A framework for in-situ geometric data acquisition using laser scanning for BIM modelling. J. Build. Eng. 2020, 28, 101073. [Google Scholar] [CrossRef]
  135. Ham, N.; Lee, S.-H. Empirical Study on Structural Safety Diagnosis of Large-Scale Civil Infrastructure Using Laser Scanning and BIM. Sustainability 2018, 10, 4024. [Google Scholar] [CrossRef] [Green Version]
  136. Mill, T.; Alt, A.; Liias, R. Combined 3D building surveying techniques–terrestrial laser scanning (TLS) and total station sur-veying for BIM data management purposes. J. Civ. Eng. Manag. 2013, 19, S23–S32. [Google Scholar] [CrossRef]
  137. Alizadehsalehi, S.; Yitmen, I.; Celik, T.; Arditi, D. The effectiveness of an integrated BIM/UAV model in managing safety on construction sites. Int. J. Occup. Saf. Ergon. 2020, 26, 829–844. [Google Scholar] [CrossRef]
  138. Castro-Lacouture, D. Construction Automation; Springer: Berlin/Heidelberg, Germany, 2009; pp. 1063–1078. [Google Scholar]
  139. Kim, M.J.; Chi, H.-L.; Wang, X.; Ding, L. Automation and Robotics in Construction and Civil Engineering. J. Intell. Robot. Syst. 2015, 79, 347–350. [Google Scholar] [CrossRef] [Green Version]
  140. Mahbub, R. An Investigation into the Barriers to the Implementation of Automation and Robotics Technologies in the Construction Industry. Ph.D. Thesis, Queensland University of Technology, Brisbane City, QLD, Australia, 2008. [Google Scholar]
  141. Delgado, J.M.D.; Oyedele, L.; Ajayi, A.; Akanbi, L.; Akinade, O.; Bilal, M.; Owolabi, H. Robotics and automated systems in construction: Understanding industry-specific challenges for adoption. J. Build. Eng. 2019, 26, 100868. [Google Scholar] [CrossRef]
  142. Hosmer, G.L. Report of progress on accelerometer for recording earthquake-shocks. Trans. Am. Geophys. Union 1931, 12, 70–71. [Google Scholar] [CrossRef]
  143. Brynjolfsson, E.; McAfee, A. Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy; Digital Frontier Press: USA, 2011. [Google Scholar]
  144. Kensek, K.; Noble, D. Building Information Modeling: BIM in Current and Future Practice; John Wiley & Sons: Hoboken, NJ, USA, 2014. [Google Scholar]
  145. Li, J.; Afsari, K.; Li, N.; Peng, J.; Wu, Z.; Cui, H. A review for presenting building information modeling education and research in China. J. Clean. Prod. 2020, 259, 120885. [Google Scholar] [CrossRef]
  146. Saini, V.; Mhaske, S. BIM an emerging technology in AEC industry for time optimization. Int. J. Struct. Civ. Eng. Res. 2013, 2, 196–200. [Google Scholar]
  147. Succar, B. Building information modelling framework: A research and delivery foundation for industry stakeholders. Autom. Constr. 2009, 18, 357–375. [Google Scholar] [CrossRef]
  148. Barazzetti, L.; Banfi, F. BIM and GIS: When parametric modeling meets geospatial data. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, IV-5/W1, 1–8. [Google Scholar] [CrossRef] [Green Version]
  149. Carneiro, J.; Rossetti, R.J.F.; Silva, D.C.; Oliveira, E.C. BIM, GIS, IoT, and AR/VR Integration for Smart Maintenance and Management of Road Networks: A Review. In 2018 IEEE International Smart Cities Conference (ISC2); Institute of Electrical and Electronics Engineers: New York, NY, USA, 2018; pp. 1–7. [Google Scholar]
  150. Yang, S.; Cui, G.; Lu, S. Ambidextrous learning of engineering project team: Relying on control or BIM AI VR AR MR? Int. J. Eng. Bus. Manag. 2020, 12, 1847979020948749. [Google Scholar] [CrossRef]
  151. Lee, S.H.; Woo, S.H.; Ryu, J.R.; Choo, S.Y. Automated building occupancy authorization using BIM and UAV-based spatial information: Photogrammetric reverse engineering. J. Asian Arch. Build. Eng. 2019, 18, 151–158. [Google Scholar] [CrossRef]
  152. Lee, G.; Sacks, R.; Eastman, C.M. Specifying parametric building object behavior (BOB) for a building information modeling system. Autom. Constr. 2006, 15, 758–776. [Google Scholar] [CrossRef]
  153. Abdellatif, M.; Al-Shamma’A, A. Review of sustainability in buildings. Sustain. Cities Soc. 2015, 14, 171–177. [Google Scholar] [CrossRef]
  154. Weerasinghe, A.S.; Ramachandra, T. Economic sustainability of green buildings: A comparative analysis of green vs non-green. Built Environ. Proj. Asset Manag. 2018, 8, 528–543. [Google Scholar] [CrossRef]
  155. Worzala, E.; Bond, S. Barriers and drivers to green buildings in Australia and New Zealand. J. Prop. Invest. Financ. 2011, 29, 494–509. [Google Scholar]
  156. Sharma, M. Development of a ‘Green building sustainability model’for Green buildings in India. J. Clean. Prod. 2018, 190, 538–551. [Google Scholar] [CrossRef]
  157. Smith, C.; Levermore, G. Designing urban spaces and buildings to improve sustainability and quality of life in a warmer world. Energy Policy 2008, 36, 4558–4562. [Google Scholar] [CrossRef]
  158. Asif, M. Growth and sustainability trends in the buildings sector in the GCC region with particular reference to the KSA and UAE. Renew. Sustain. Energy Rev. 2016, 55, 1267–1273. [Google Scholar] [CrossRef]
  159. Naji, H.I.; Mahmood, M.S.; Mohammad, H.E. Using BIM to propose building alternatives towards lower consumption of electric power in Iraq. Asian J. Civ. Eng. 2019, 20, 669–679. [Google Scholar] [CrossRef]
  160. Wang, Q.; Su, M. Drivers of decoupling economic growth from carbon emission–an empirical analysis of 192 countries using decoupling model and decomposition method. Environ. Impact Assess. Rev. 2020, 81, 106356. [Google Scholar] [CrossRef]
  161. Nawari, N.O.; Ravindran, S. Blockchain and Building Information Modeling (BIM): Review and Applications in Post-Disaster Recovery. Buildings 2019, 9, 149. [Google Scholar] [CrossRef] [Green Version]
  162. Puthal, D.; Malik, N.; Mohanty, S.P.; Kougianos, E.; Yang, C. The Blockchain as a Decentralized Security Framework [Future Directions]. IEEE Consum. Electron. Mag. 2018, 7, 18–21. [Google Scholar] [CrossRef]
  163. Llewellyn, J.; Leonard, G.; Jackson, R. Meeting New Zealand’s Peace and Security Challenges Through Disarmament and Nonviolence; Springer Science and Business Media LLC: Berlin/Heidelberg, Germany, 2019; pp. 345–361. [Google Scholar]
  164. Volk, R.; Stengel, J.; Schultmann, F. Building Information Modeling (BIM) for existing buildings—Literature review and future needs. Autom. Constr. 2014, 38, 109–127. [Google Scholar] [CrossRef] [Green Version]
  165. Becerik-Gerber, B.; Jazizadeh, F.; Li, N.; Calis, G. Application areas and data requirements for BIM-enabled facilities man-agement. J. Constr. Eng. Manag. 2012, 138, 431–442. [Google Scholar] [CrossRef]
  166. Akcamete, A.; Akinci, B.; Garrett, J.H. Potential utilization of building information models for planning maintenance activi-ties. In Proceedings of the International Conference on Computing in Civil and Building Engineering, Nottingham, UK, 30 June–2 July 2010; pp. 151–157. [Google Scholar]
  167. Kiviniemi, M.; Sulankivi, K.; Kähkönen, K.; Mäkelä, T.; Merivirta, M.-L. BIM-Based Safety Management and Communication for Building Construction; VTT: Espoo, Finland, 2011. [Google Scholar]
  168. Deng, L.; Zhong, M.; Liao, L.; Peng, L.; Lai, S. Research on Safety Management Application of Dangerous Sources in Engi-neering Construction Based on BIM Technology. Adv. Civ. Eng. 2019. [Google Scholar] [CrossRef] [Green Version]
  169. Gong, X.; Michel, P.; Cantin, R. Multiple-criteria decision analysis of BIM influences in building energy management. Build. Simul. 2019, 12, 641–652. [Google Scholar] [CrossRef]
  170. Aram, S.; Eastman, C.; Sacks, R. Requirements for BIM platforms in the concrete reinforcement supply chain. Autom. Constr. 2013, 35, 1–17. [Google Scholar] [CrossRef]
  171. Chen, P.-H.; Nguyen, T.C. A BIM-WMS integrated decision support tool for supply chain management in construction. Autom. Constr. 2019, 98, 289–301. [Google Scholar] [CrossRef]
  172. Boukamp, F.; Akinci, B. Automated processing of construction specifications to support inspection and quality control. Autom. Constr. 2007, 17, 90–106. [Google Scholar] [CrossRef]
  173. Arayici, Y. Towards building information modelling for existing structures. Struct. Surv. 2008, 26, 210–222. [Google Scholar] [CrossRef] [Green Version]
  174. Tashakkori, H.; Rajabifard, A.; Kalantari, M. A new 3D indoor/outdoor spatial model for indoor emergency response facilitation. Build. Environ. 2015, 89, 170–182. [Google Scholar] [CrossRef]
  175. Li, N.; Becerik-Gerber, B.; Krishnamachari, B.; Soibelman, L. A BIM centered indoor localization algorithm to support building fire emergency response operations. Autom. Constr. 2014, 42, 78–89. [Google Scholar] [CrossRef]
  176. Woo, J.-H.; Menassa, C. Virtual Retrofit Model for aging commercial buildings in a smart grid environment. Energy Build. 2014, 80, 424–435. [Google Scholar] [CrossRef]
  177. Turk, Ž.; Klinc, R. Potentials of Blockchain Technology for Construction Management. Procedia Eng. 2017, 196, 638–645. [Google Scholar] [CrossRef]
  178. Mousa, M.; Luo, X.; McCabe, B. Utilizing BIM and Carbon Estimating Methods for Meaningful Data Representation. Procedia Eng. 2016, 145, 1242–1249. [Google Scholar] [CrossRef] [Green Version]
  179. Gan, V.J.; Deng, M.; Tse, K.; Chan, C.; Lo, I.M.; Cheng, J.C. Holistic BIM framework for sustainable low carbon design of high-rise buildings. J. Clean. Prod. 2018, 195, 1091–1104. [Google Scholar] [CrossRef]
  180. Eastman, C.M.; Eastman, C.; Teicholz, P.; Sacks, R.; Liston, K. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
  181. Guo, H.; Yu, R.; Fang, Y. Analysis of negative impacts of BIM-enabled information transparency on contractors’ interests. Autom. Constr. 2019, 103, 67–79. [Google Scholar] [CrossRef]
  182. Babič, N. Čuš; Rebolj, D.; Nekrep-Perc, M.; Podbreznik, P. Supply-chain transparency within industrialized construction projects. Comput. Ind. 2014, 65, 345–353. [Google Scholar] [CrossRef]
  183. Chen, S.; Liang, Y.-C.; Sun, S.; Kang, S.; Cheng, W.; Peng, M. Vision, Requirements, and Technology Trend of 6G: How to Tackle the Challenges of System Coverage, Capacity, User Data-Rate and Movement Speed. IEEE Wirel. Commun. 2020, 27, 218–228. [Google Scholar] [CrossRef] [Green Version]
  184. Collins, R.; Zhang, S.; Kim, K.; Teizer, J. Integration of Safety Risk Factors in BIM for Scaffolding Construction. Comput. Civ. Build. Eng. 2014, 307–314. [Google Scholar] [CrossRef] [Green Version]
  185. Swallow, M.; Zulu, S. Students’ awareness and perception of the value of BIM and 4D for site health and safety management. J. Eng. Des. Technol. 2019, 18, 414–430. [Google Scholar] [CrossRef]
  186. Olawumi, T.O.; Chan, D.W. Development of a benchmarking model for BIM implementation in developing countries. Benchmarking Int. J. 2019, 26, 1210–1232. [Google Scholar] [CrossRef]
  187. Rao, S.K.; Prasad, R. Impact of 5G Technologies on Industry 4.0. Wirel. Pers. Commun. 2018, 100, 145–159. [Google Scholar] [CrossRef]
  188. Chew, M.; Teo, E.; Shah, K.; Kumar, V.; Hussein, G. Evaluating the Roadmap of 5G Technology Implementation for Smart Building and Facilities Management in Singapore. Sustainability 2020, 12, 10259. [Google Scholar] [CrossRef]
  189. Huang, T.; Yang, W.; Wu, J.; Ma, J.; Zhang, X.; Zhang, D. A Survey on Green 6G Network: Architecture and Technologies. IEEE Access 2019, 7, 175758–175768. [Google Scholar] [CrossRef]
  190. Chan, A.P.C.; Darko, A.; Olanipekun, A.O.; Ameyaw, E.E. Critical barriers to green building technologies adoption in de-veloping countries: The case of Ghana. J. Clean. Prod. 2018, 172, 1067–1079. [Google Scholar] [CrossRef]
  191. Okpala, I.; Nnaji, C.; Karakhan, A.A. Utilizing Emerging Technologies for Construction Safety Risk Mitigation. Pract. Period. Struct. Des. Constr. 2020, 25, 04020002. [Google Scholar] [CrossRef]
  192. Perry, P.; Kent, A.; Bonetti, F. The Use of Mobile Technologies in Physical Stores: The Case of Fashion Retailing; Springer: Berlin/Heidelberg, Germany, 2018; pp. 169–195. [Google Scholar]
  193. Olawumi, T.O.; Chan, D.W.; Wong, J.K.; Chan, A.P. Barriers to the integration of BIM and sustainability practices in construction projects: A Delphi survey of international experts. J. Build. Eng. 2018, 20, 60–71. [Google Scholar] [CrossRef]
  194. Marefat, A.; Toosi, H.; Hasankhanlo, R.M. A BIM approach for construction safety: Applications, barriers and solutions. Eng. Constr. Arch. Manag. 2019, 26, 1855–1877. [Google Scholar] [CrossRef]
  195. Babatunde, S.O.; Udeaja, C.; Adekunle, A.O. Barriers to BIM implementation and ways forward to improve its adoption in the Nigerian AEC firms. Int. J. Build. Pathol. Adapt. 2020, 39, 48–71. [Google Scholar] [CrossRef] [Green Version]
  196. Maina, J.J. Barriers to effective use of CAD and BIM in architecture education in Nigeria. Int. J. Built Environ. Sustain. 2018, 5. [Google Scholar] [CrossRef]
  197. Zhao, X.; Wu, P.; Wang, X. Risk paths in BIM adoption: Empirical study of China. Eng. Constr. Arch. Manag. 2018, 25, 1170–1187. [Google Scholar] [CrossRef]
  198. Liu, S.; Xie, B.; Tivendale, L.; Liu, C. Critical Barriers to BIM Implementation in the AEC Industry. Int. J. Mark. Stud. 2015, 7, 162. [Google Scholar] [CrossRef]
  199. Al-Yami, A.; Sanni-Anibire, M.O. BIM in the Saudi Arabian construction industry: State of the art, benefit and barriers. Int. J. Build. Pathol. Adapt. 2019, 39, 33–47. [Google Scholar] [CrossRef]
  200. Oraee, M.; Hosseini, M.R.; Edwards, D.J.; Li, H.; Papadonikolaki, E.; Cao, D. Collaboration barriers in BIM-based construction networks: A conceptual model. Int. J. Proj. Manag. 2019, 37, 839–854. [Google Scholar] [CrossRef]
  201. Farooq, U.; Rehman, S.K.U.; Javed, M.F.; Jameel, M.; Aslam, F.; Alyousef, R. Investigating BIM Implementation Barriers and Issues in Pakistan Using ISM Approach. Appl. Sci. 2020, 10, 7250. [Google Scholar] [CrossRef]
  202. Kassem, M.; Brogden, T.; Dawood, N. BIM and 4D planning: A holistic study of the barriers and drivers to widespread adoption. J. Constr. Eng. Proj. Manag. 2012, 2, 1–10. [Google Scholar] [CrossRef] [Green Version]
  203. Georgiadou, M.C. An overview of benefits and challenges of building information modelling (BIM) adoption in UK residential projects. Constr. Innov. 2019, 19, 298–320. [Google Scholar] [CrossRef]
  204. Chan, D.W.; Olawumi, T.O.; Ho, A.M. Perceived benefits of and barriers to Building Information Modelling (BIM) implementation in construction: The case of Hong Kong. J. Build. Eng. 2019, 25, 100764. [Google Scholar] [CrossRef]
  205. Babatunde, S.O.; Ekundayo, D. Barriers to the incorporation of BIM into quantity surveying undergraduate curriculum in the Nigerian universities. J. Eng. Des. Technol. 2019, 17, 629–648. [Google Scholar] [CrossRef]
  206. Lindblad, H. Black boxing BIM: The public client’s strategy in BIM implementation. Constr. Manag. Econ. 2018, 37, 1–12. [Google Scholar] [CrossRef] [Green Version]
  207. Vass, S.; Gustavsson, T.K. Challenges when implementing BIM for industry change. Constr. Manag. Econ. 2017, 35, 597–610. [Google Scholar] [CrossRef]
  208. Enshassi, A.; Abu Hamra, L.A. Challenges to the Utilization of BIM in the Palestinian Construction Industry. In Proceedings of the International Symposium on Automation and Robotics in Construction, Taipei, Taiwan, 28 June–1 July 2017. [Google Scholar]
  209. Jalaei, F.; Jrade, A.; Nassiri, M. Integrating decision support system (DSS) and building information modeling (BIM) to optimize the selection of sustainable building components. J. Inf. Technol. Constr. 2015, 20, 399–420. [Google Scholar]
  210. Kim, K.; Cho, Y.; Kim, K. BIM-driven automated decision support system for safety planning of temporary structures. J. Constr. Eng. Manag. 2018, 144, 4018072. [Google Scholar] [CrossRef]
  211. Al-Kasasbeh, M.; Abudayyeh, O.; Liu, H. An integrated decision support system for building asset management based on BIM and Work Breakdown Structure. J. Build. Eng. 2021, 34, 101959. [Google Scholar] [CrossRef]
  212. Love, P.E.; Matthews, J.; Lockley, S. BIM for Built Asset Management. Built Environ. Proj. Asset Manag. 2015, 5. [Google Scholar] [CrossRef]
  213. Khosrowshahi, F.; Arayici, Y. Roadmap for implementation of BIM in the UK construction industry. Eng. Constr. Arch. Manag. 2012, 19, 610–635. [Google Scholar] [CrossRef] [Green Version]
  214. Kaufmann, D.; Vicente, P.C. Legal corruption. Econ. Polit. 2011, 23, 195–219. [Google Scholar] [CrossRef] [Green Version]
  215. De Jong, M.; Henry, W.P.; Stansbury, N. Eliminating Corruption in Our Engineering/Construction Industry. Leadersh. Manag. Eng. 2009, 9, 105–111. [Google Scholar] [CrossRef]
  216. Ameyaw, E.E.; Pärn, E.; Chan, A.P.; Manu, D.-G.O.; Edwards, D.J.; Darko, A. Corrupt Practices in the Construction Industry: Survey of Ghanaian Experience. J. Manag. Eng. 2017, 33, 05017006. [Google Scholar] [CrossRef]
  217. May, D.; Wilson, O.; Skitmore, M. Bid cutting: An empirical study of practice in South-East Queensland. Eng. Constr. Archit. Manag. 2001, 8, 250–256. [Google Scholar]
  218. Tabish, S.; Jha, K.N. Analyses and evaluation of irregularities in public procurement in India. Constr. Manag. Econ. 2011, 29, 261–274. [Google Scholar] [CrossRef]
  219. Osei-Tutu, E.; Badu, E.; Owusu-Manu, D. Exploring corruption practices in public procurement of infrastructural projects in Ghana. Int. J. Manag. Proj. Bus. 2010, 3, 236–256. [Google Scholar] [CrossRef]
  220. Alutu, O.E. Unethical Practices in Nigerian Construction Industry: Prospective Engineers’ Viewpoint. J. Prof. Issues Eng. Educ. Pract. 2007, 133, 84–88. [Google Scholar] [CrossRef]
  221. Chotibhongs, R.; Arditi, D. Detection of Collusive Behavior. J. Constr. Eng. Manag. 2012, 138, 1251–1258. [Google Scholar] [CrossRef]
  222. Doree, A.G. Collusion in the Dutch construction industry: An industrial organization perspective. Build. Res. Inf. 2004, 32, 146–156. [Google Scholar] [CrossRef]
  223. Hartley, R. Fighting Corruption in the Australian Construction Industry: The National Code of Practice. Leadersh. Manag. Eng. 2009, 9, 131–135. [Google Scholar] [CrossRef]
  224. Brown, J.S.; Loosemore, M. Behavioural factors influencing corrupt action in the Australian construction industry. Eng. Constr. Arch. Manag. 2015, 22, 372–389. [Google Scholar] [CrossRef]
  225. Bowen, P.; Pearl, R.; Akintoye, A. Professional ethics in the South African construction industry. Build. Res. Inf. 2007, 35, 189–205. [Google Scholar] [CrossRef]
  226. Zarkada-Fraser, A.; Skitmore, M. Decisions with moral content: Collusion. Constr. Manag. Econ. 2000, 18, 101–111. [Google Scholar] [CrossRef] [Green Version]
  227. Bowen, P.; Akintoye, A.; Pearl, R.; Edwards, P.J. Ethical behaviour in the South African construction industry. Constr. Manag. Econ. 2007, 25, 631–648. [Google Scholar] [CrossRef]
  228. Ardiny, H.; Witwicki, S.; Mondada, F. Are autonomous mobile robots able to take over construction? A review. Int. J. Robot. 2015, 4, 10–21. [Google Scholar]
Ijerph 18 06135 g001 550
Figure 1. Research flowchart design.
Figure 1. Research flowchart design.
Ijerph 18 06135 g001
Ijerph 18 06135 g002 550
Figure 2. The annual publications trend.
Figure 2. The annual publications trend.
Ijerph 18 06135 g002
Ijerph 18 06135 g003 550
Figure 3. Journal Contributions.
Figure 3. Journal Contributions.
Ijerph 18 06135 g003
Ijerph 18 06135 g004 550
Figure 4. The geospatial collaboration network in research on DTs in the AEC industry.
Figure 4. The geospatial collaboration network in research on DTs in the AEC industry.
Ijerph 18 06135 g004
Ijerph 18 06135 g005 550
Figure 5. Research interests on DTs in the AEC industry (keywords co-occurrence).
Figure 5. Research interests on DTs in the AEC industry (keywords co-occurrence).
Ijerph 18 06135 g005
Ijerph 18 06135 g006 550
Figure 6. Mapping of DTs in the AEC industry.
Figure 6. Mapping of DTs in the AEC industry.
Ijerph 18 06135 g006
Ijerph 18 06135 g007 550
Figure 7. Summary of findings.
Figure 7. Summary of findings.
Ijerph 18 06135 g007
Table
Table 1. Literature searching strategies.
Table 1. Literature searching strategies.
TopicKeywordsResults
the AEC industry“Digital” OR “technology” OR “technologies” AND “Architectural engineering” OR “Construction engineering” OR “Civil engineering”11,047
Digital technologies “BIM” OR “Building information modeling” OR “Building information modelling” OR “AR” OR “VR” OR “RFID” OR “Photogrammetry” OR “Images” OR “Videos” OR “Cameras” OR “laser scanning” OR “3D scanning” OR “Robotics” OR “Block chain”1956
Digital technologies“Digital” OR “technology” OR “technologies” AND “Architectural engineering” OR “Construction engineering” OR “Civil engineering” AND “BIM” OR “Building information modeling” OR “Building information modelling” OR “AR” OR “VR” OR “RFID” OR “Photogrammetry” OR “Images” OR “Videos” OR “Cameras” OR “laser scanning” OR “3D scanning” OR “Robotics” OR “Block chain”1778
Digital technologies and the AEC industryScreening on the basis of engineering fields, journal papers, unrelated topics, non-English publications200
Table
Table 2. Geospatial distribution of research articles on DTs in the AEC industry.
Table 2. Geospatial distribution of research articles on DTs in the AEC industry.
CountriesResearch ArticlesAverage Publication Year
United States602010
China332017
Spain182016
United Kingdom182016
Australia162017
South Korea 132014
Hong Kong92015
Canada 82012
Germany62016
Taiwan62014
Israel52007
Poland42015
Brazil32018
Greece32009
Iran32018
New Zealand32019
Austria22002
Finland22013
Hungary22015
India22020
Italy22020
Singapore22016
Switzerland22007
Table
Table 3. Top keywords of research interest on DTs in the AEC industry.
Table 3. Top keywords of research interest on DTs in the AEC industry.
KeywordsOccurrencesAverage Publication YearLinksTotal Links Strength
BIM6020171946
Construction management1120131116
Civil engineering92017710
Photogrammetry9201666
Construction82010913
Augmented reality82014712
Information technology62015810
Automation6200779
Computer vision6201667
Inspection4201566
Curriculum4201159
Tracking4201255
Visualization4201346
Mobile computing4201346
Construction site4201333
Artificial intelligence4201333
Unmanned aerial vehicle4201734
Architectural engineering4201924
Case study4201724
Construction education4201526
Digital image correlation4201822
Terrestrial laser scanning4201622
Computer applications3201477
Buildings3201655
Displacement measurement3201645
Education3201647
Damage3201533
LiDAR3201733
Mapping3200622
Construction technology3201924
Structural health monitoring3201723
Point cloud3201511
Rfid3201211
Text mining3201912
Table
Table 4. Summary of new advancements towards DTs.
Table 4. Summary of new advancements towards DTs.
New AdvancementsReferences
Facility management activities[164,165,166]
Safety Management[167,168]
Energy management[169]
Supply chain management[170,171]
Quality control[172]
Emergency management[173,174,175]
Retrofit planning[136,176]
BIM–block chain integration[177]
Carbon emissions[178,179]
Monitoring and Tracking assessment[165,173,180]
Transparency and Security[181,182]
Mobile technology[183]
Table
Table 5. DTS adoption challenges in the AEC industry.
Table 5. DTS adoption challenges in the AEC industry.
ChallengesReferences
Inadequate expertise[193,194]
Inadequate government policies[195,196]
Cultural barriers[197]
Cost[198,199]
Inadequate demand from clients[200]
Resistance to change[201,202]
Security risks[198,202,203]
Inadequate staff[204,205]
Competing initiatives[206,207]
Lack of industry standards[194,208]
Lack of decision support tools[209,210,211]
Liability concerns[212,213]
Table
Table 6. Negative attitude towards DTs in the AEC industry.
Table 6. Negative attitude towards DTs in the AEC industry.
Negative AttitudesReferences
Bribery[219]
Kickbacks[220]
Collusive tendering and bid rigging[221,222]
Embezzlement [223]
Conflict of interest[224,225]
Hidden tender prices[226]
Bid cutting[221,227]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Manzoor, B.; Othman, I.; Pomares, J.C. Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry—A Bibliometric—Qualitative Literature Review of Research Activities. Int. J. Environ. Res. Public Health 2021, 18, 6135. https://doi.org/10.3390/ijerph18116135

AMA Style

Manzoor B, Othman I, Pomares JC. Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry—A Bibliometric—Qualitative Literature Review of Research Activities. International Journal of Environmental Research and Public Health. 2021; 18(11):6135. https://doi.org/10.3390/ijerph18116135

Chicago/Turabian Style

Manzoor, Bilal, Idris Othman, and Juan Carlos Pomares. 2021. "Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry—A Bibliometric—Qualitative Literature Review of Research Activities" International Journal of Environmental Research and Public Health 18, no. 11: 6135. https://doi.org/10.3390/ijerph18116135

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop