Energy Consumption of Retrofitting Existing Public Buildings in Malaysia under BIM Approach: Pilot Study

Abstract

Building information modeling (BIM) platforms to enhance design and construction processes have been rising recently, with BIM-based tools such as Autodesk Revit’s Architecture. The importance of BIM can be mainly seen in reducing energy consumption by at least 30%, leading to a huge cut in carbon dioxide, and saving the environment. BIM helps engineers and contractors to use less material for better benefits for stakeholders, including organizations, governmental offices, and businesses. This study investigates the reliability and validity of a constructed questionnaire to pre-determine the applications relevant to a questionnaire to be used in a large-scale study. The literature has highlighted the connection between BIM and energy-driven retrofits. However, the application of BIM to the retrofitting of existing structures confronts obstacles, which may be attributable to the multidisciplinary character of information sharing, the timeliness of communication, and the large number of technology components required to provide an optimal exchange. A pilot study was conducted, identifying the sample size of 30 random respondents out of 167 samples. SPSS was used for estimating the percentages of the demographic attributes for the respondents, the face validity, internal-consistency validity, the validation of all contracts, and Pearson’s correlation. The results show that engineers constitute 46%, project managers (20%), contractors (17%), and the rest (approximately 17%) are divided among other professionals. The validity of internal consistency ranges from 0.791 to 0.912, which reflects perfect consistency. The internal consistency of each part was recorded at 0.942 (energy), 0.957 (strategies), and 0.979 (framework). The validation for the energy part ranges from 0.610 to 0.912; for strategies (0.451 to 0.884,) and for the framework (0.681 to 0.884). Pearson’s correlation for all 17 questions showed a minimum value of 0.464, while the maximum value was 0.890. The results show that all questionnaire elements were successfully validated with a Cronbach alpha factor mainly higher than 0.6—the threshold accepted by most researchers. Hence, the work on the broader scale of testing and analysis could proceed.

1. Introduction

The term ‘pilot studies’ means mini versions of a full-scale study (also called feasibility studies) and the specific pre-testing of a particular research instrument such as a questionnaire or interview schedule. Determining the feasibility of the research design could be approached by conducting a pilot study before starting [1]. The results of the pilot study can guide researchers in testing the methodology before a large-scale investigation could be implemented. Pilot studies might be performed using either qualitative or quantitative methods, or both [2]. Scientific research does not always go as planned; therefore, it should optimize the process to minimize unforeseen events, without possible risk [3]. Risk is disastrous, and expensive mistakes could be discovered and corrected in a pilot study. Pilot work gives not only a chance to determine whether the project is feasible, but also an opportunity to publish the corresponding results. Pilot studies should be guided by an ethical and scientific obligation to obtain the required information to assist other researchers in making the most of their resources. This pilot study focuses on the energy consumption of retrofitting existing public buildings in Malaysia under the BIM approach.

Energy is being used up throughout the world, especially in developing countries. Consequently, energy-supply challenges, dwindling energy-supplies, and substantial environmental-repercussions influence global warming, ozone depletion, and climate change. Non-residential buildings such as educational facilities, offices, or hospitals consume energy more than residential buildings [4]. Environmental comfort is tightly connected to energy efficiency [5]. A building is considered energy efficient when it offers users an adequate degree of environmental comfort while using low energy [6]. In tropical regions, public buildings, particularly those with air conditioning, use a significant amount of energy. One of the goals of researchers is to reduce carbon intensiveness to 4% by 2020, compared with 2005, and decrease energy usage to 40% by 2050 [7]. Malaysia, in particular, needs more energy, since its capability in terms of economics and technology is growing quickly.

Public knowledge and concern about the impact of building on the environment, labour efficiency, and public health, are developing in Malaysia. Thus, the public and private sectors have begun to demand more energy-efficient, resource-efficient, and high-quality interior environments. Malaysia’s government and people have raised awareness and expressed concern about innovative and sustainable housing-developments [7]. The Malaysian government issued legislation under the CITP 2016–2020, in which the government aimed to create a sustainable construction-industry and improve the life-cycle-performance of buildings. The importance of this legislation can be seen in the following quotation: “the construction industry is crucial to the Malaysian economy and its growth. The construction industry currently contributes 4 per cent to the Malaysian Gross Domestic Product (GDP) and is expected to contribute 5.5 per cent to the Malaysian GDP up to 2020” [8].

There is a need for strategies or approaches to mitigate the negative impacts on the environment of development, construction, and urbanisation [9]. Retrofitting an existing structure is one of the most environmentally-friendly, sustainable, and cost-effective ways to maximise an existing building’s energy performance [10,11]. Renovation, on the other hand, is the most hazardous, complicated, and unpredictable project to manage [12]. The construction industry’s rehabilitation process is fragmented, resulting in the loss of integration of disparate data [13].

Adopting BIM for retrofitting existing structures is a new research topic, and current research, particularly in Malaysia, is still premature [14]. Researchers examine the use of BIM in the retrofitting and maintenance of existing structures [15]. Additionally, the use of BIM to retrofit existing buildings is a hot research-topic, with researchers emphasising it as the future approach for energy-retrofit studies [16,17,18]. As a result, this paper attempts to fill in this gap. The determinants used for estimating energy are shown in Figure 1.

Furthermore, BIM is a set of technologies, policies and processes assimilated to enable the management of vital project-data in a digital format through the course of a building life-cycle [19]. BMI is used to model buildings and run several analyses sequentially, to estimate the energy performance of various retrofit approaches in existing buildings [20,21]. On the other hand, the AECO sector is currently responsible for a significant portion of global energy-consumption [22]. As a result, its daily operations have a number of negative environmental consequences [23]. Consequently, the AECO sector is under tremendous pressure to reduce polluting emissions [24].

Since buildings require energy for heating and cooling, ventilation, and lighting, there is an urgent need to limit consumption in existing buildings. Buildings must have adequate natural aeration and ventilation, natural lighting, and effective and efficient heating/cooling systems [25]. Nonetheless, the primary impediments to retrofitting are its complexity and a dearth of proper research and strategy. Regardless of the complexity of retrofitting, existing buildings should be investigated for their environmental friendliness and potential to provide a more efficient technique for optimizing energy in less-efficient buildings [26]. However, there are numerous other obstacles to green-retrofitting existing buildings, including a lack of models for retrofit methods, an insufficient number of energy-optimization procedures, a lack of life-cycle cost analysis, and a low return on investment [27]. Therefore, complete sets of detailed data on the direct and indirect consequences of retrofitting on the environment, are required. The cost efficiency, maintenance requirements, impact on end-users, and effectiveness of the renovated building were all critical considerations [9].

In this study, a survey questionnaire on BIM retrofitting in existing government buildings was developed, to extract stakeholders’ perspectives and impressions on BIM retrofitting. The fundamental prerequisites for successfully retrofitting government buildings are met, yet there is a scarcity of studies on BIM models for retrofitting in the maintenance or improvement of government buildings in Malaysia. The current analysis demonstrates the critical need for a framework to promote BIM use for upgrading existing government buildings effectively.

2. Literature Review

2.1. Building Information Modelling (BIM)

Several professionals have characterized BIM in a variety of ways. Researchers and users review BIM as a software tool or as a process for creating and recording information about buildings [28]. Meanwhile, there is widespread agreement that BIM is a holistic approach to facilitate design, construction, and maintenance. As defined by [29], BIM is a growing technology in the architecture, engineering, and construction (AEC) sector, and has been used in a wide range of academic applications, including project planning, structural design, and facility management. As an effective method for collaborative building-design and construction [30,31], BIM could provide the following:

• Enhanced time-management, including improved workflows, completely automated low-level procedures, and a concentration on high value-added services.
• Added value, i.e., producing value for the customer beyond the minimal deliverables.
• Enhanced cooperation, characterized by a high degree of communication, transparency, and teamwork, for the overall good of the project.
• Decision making that is holistic across disciplines and design domains.
• The ability to assist its consumers in acquiring a more energy-efficient structure.
• A demonstration of the physical and functional characteristics of the technology that link project-information databases in each field.
• Faster access to information and relevant documents for all participants of the construction project.
• An increase in employee productivity, financial control and the quality of the documents.

BIM methodology is developed and utilized chiefly for new construction projects. A centralized digital-model may provide a platform for diverse sectors to collaborate, exchange information, and communicate, under a common framework [32]. The building-retrofit optimization task is to identify, develop, and implement the most cost-effective retrofit solutions to improve energy performance while maintaining adequate service-levels and acceptable interior thermal-comfort. Existing structures consume the most incredible energy in the building sector, while new construction replaces around 1.0–3.0% of existing structures, yearly [33]. However, existing buildings are a significant source of excessive energy-consumption in Malaysia. Aged buildings require more energy to operate because their performance level has decreased over time [34]. However, the importance of existing structures for long-term sustainability should not be underestimated [9]. As a structure ages, its energy consumption requires more energy for building operations, as its performance deteriorates over time [35].

On the other hand, demolition generates more garbage, because demolition waste is twice as large as construction waste [7]. Most garbage is deposited in landfills in Malaysia, raising economic, environmental, and social concerns [36]. According to [10], retrofitting’s primary concerns are energy, water usage, and production waste. A light-touch retrofit, for example, may save up to 30–40% on annual energy-costs by installing energy-efficient lighting and controls, building-services controls, and management systems.

2.2. Old Building Energy Consumption

Many countries have established their green-rating systems based on their appropriateness for populace benefits, and this progress is seen as the world’s target in greening the earth. According to the International Energy Agency (IEA), the building sector contributes to approximately 30% of global carbon emissions, and over 30% of the total energy-consumption worldwide [37]. Existing buildings consume a large percentage of the total energy, owing to their poor energy-performance [38]. As Malaysia moves towards a sustainable lifestyle and development, the need to prepare for the change is imperative. Sustainability has become an important initiative discussed and undertaken not only by private buildings, but also by public buildings used for residential, office, and commercial purposes, as well as hospitals.

Malaysia is one of the South East Asian economies with the fastest growth-rate [39]. With a world-class infrastructure, a significant amount of a country’s investment is allocated to physical infrastructure. Therefore, it is of primary importance that these facilities, which include public buildings, are maintained, to serve the architectural and aesthetical functions for which they are built. Malaysia’s government and people have raised awareness and concern about innovative and sustainable housing-developments [7]. There is a need for strategies or approaches to mitigate the negative impacts on the environment of development, construction, and urbanization [9]. The physical appearance of public building institutions constitutes the basis upon which society judges the quality of services offered. However, despite the heavy investment in public buildings, public institutions allow their structures to be taken care of with a minimum budget on a sustainable maintenance-plan, to preserve the quality of the buildings.

Energy retrofitting of older buildings is currently a top priority for developed countries. Globally, countries invest in effective retrofit-solutions to meet their energy-efficiency requirements [14]. Because historical structures were constructed according to earlier rules and regulations, the current structure would require many retrofit assessments to meet modern standards such as BIM. Retrofitting an existing structure entails incorporating new technologies or features to enhance the structure’s functionality and efficiency, by inserting new structural elements [34]. Budget limits, limited information, different uncertainties, and a lack of understanding and faith in new technology for upgrading, are the primary impediments [40].

Energy is required in almost all our daily life, including agriculture, transportation, telecommunications, and industrial activities that influence economic growth. However, the growth in the economy in Malaysia is dependent on an uninterrupted supply of energy [41]. Since buildings require energy for heating and cooling, and ventilation, and lighting, there is an urgent need to limit the consumption in existing buildings. The high levels of energy efficiency in existing buildings can be achieved via green retrofitting; an ideal combination of multiple solutions is required [32].

Public knowledge and concern about the impact of building on the environment, labour efficiency, and public health, are developing in Malaysia. Thus, the public and private sectors have begun to demand more energy-efficient, resource-efficient, and high-quality interior environments. Malaysia’s government and people have raised awareness and concern about innovative and sustainable housing-developments [7]. Various energy-saving and pollution-reduction methods must be used to decrease energy consumption in buildings and limit negative environmental-consequences.

3. Research Objectives and Questions

The following question is to be explored in this study:

RQ1: What is the current energy-status of BIM retrofitting in public buildings?

RQ2: What strategies will facilitate the analysis of energy consumption in existing public buildings?

The following objectives are to be achieved in this study:

RO1: To investigate and examine the current energy-status of BIM retrofitting in public buildings.

RO2: To determine and examine the strategies that will facilitate the analysis of energy consumption in existing public buildings.

4. Research Methodology

This research adopts quantitative-data-collection techniques. The questionnaire was selected in this research, to collect data. A questionnaire is commonly used to gather survey data, which is often numerical, and which tends to be easy to examine [42].

This study aims to determine whether this research was driven by the critical requirement to retrofit government buildings successfully. The key factors comprise (a) the energy tatus of BIM retrofitting in public buildings; (b) strategies for analysing energy consumption; (c) the importance of decision-making factors for selecting construction materials. The questionnaire structure includes multiple-choice and rating questions. The five-point Likert scale was used for each of the previous sections to be treated statistically, as follows: strongly agree (5), agree (4), neutral (3), disagree (2), strongly disagree (1). Statistical Package for the Social Sciences software (SPSS Version 26) is used to analyse survey data. This questionnaire consists of five parts, as shown in

Table 1. Construction of the questionnaire.

Part	Details
1	A general introduction to the questionnaire and its purpose, in addition to some basic concepts related to the research.
2	Some questions to gather information about the participant’s background, related to BIM implementation.
3	Questions regarding the energy status of the processing of BIM models in public buildings. It includes three themes (18 statements).
4	Questions related to strategies for analyzing energy consumption. It includes three themes (27 statements).
5	Questions relating to the construction of the proposed framework. It includes three themes (40 statements).

.

To achieve the objective of this research, a structured questionnaire was projected at stakeholders (project managers, deputy project-managers, contractors, architects, consultants, and site engineers) working within the organizations. The literature in the study was used as a guide for developing the questions in the questionnaire. In addition, some questions in the questionnaire were quoted from other sources [43]. The details of the questionnaire are available in

Table 2. Summary of the dimension, code, and number of statements of the questionnaire.

Dimension	Code	Number of Statements	Total no of Statements
Energy Status of BIM Retro Public Buildings
Driver	ED01-04	4	18
Utilization	ET01-04	4
Barriers	EB01-06	6
Expectation	EE01-04	4
Strategies for Analyzing Energy Consumption
Benefits	SB01-03	3	27
Obstacles	SO01-06	6
Sustainability	SS01-03	3
Consideration	SC01-06	5
Heat Reduction	SR01-05	5
Energy Saving	SE01-05	5
Construction of Framework
Barriers	CB01-06	6	40
Social	CS01-04	4
Economy	CE01-03	3
Regulation	CR01-05	5
Psychology	CP01-06	6
Managerial	CM01-07	7
Technical	CT01-09	9
Total			85

.

4.1. Testing Sample Size

For this study, assume that a particular problem has a given probability of occurring in a potential study-participant. If there is a 0.10 probability of encountering unanticipated reasons for exclusion in a given participant, then there is a 0.90 probability that this problem does not manifest itself. In a group of n participants with a problem probability ($\pi$), there is then a 0.90ⁿ, the probability, ($P$), that the problem will not occur at all, as depicted in Equation (1) [44].

$$P\left(x>0\right)=1-{\left(1-\pi \right)}^n$$

(1)

The number of participants, $n$, is defined in terms of $\pi$ and the threshold of confidence, $\gamma$, as in Equation (2):

$$n=\frac{\ln \left(1-\gamma \right)}{\ln \left(1-\pi \right)}$$

(2)

For a special case, if $\pi$ is 0.10 (10%) and $\gamma$ is 0.15 (15%), then $n$ is 29. When these parameters are applied in Equation (1), the probability, $P,$ is 0.0498, which is below 5%.

One of the key reasons why a pilot study is needed, is to obtain the required preliminary data for the calculation of a sample size for the primary outcome. Pilot studies are frequently used to assist researchers to determine the appropriate sample size for the main experiment [45]. The questionnaire was designed and formulated based on a literature review. It was distributed to 167 respondents, including project managers, deputy-project managers, contractors, architects, consultants, and site engineers working within Malaysian construction companies that have adopted BIM in their projects, by e-mail and sometimes by visiting the companies directly. A total of 97 responses were obtained, and 30 were selected randomly as a pilot study. These will be excluded from the total sample. Following the pilot survey, it was discovered that two of the questions were unclear, and three were changed.

4.2. Face Validity

The Statistical Package for Social Sciences (SPSS) package was used for statistical analysis. SPSs is a revolutionary tool that can be used easily, and it can be characterized as a comprehensive statistics program which enabled researchers to conduct complex statistical analyses on big datasets, on their own [46].

The questionnaire requires content validity [47] to ensure that data generated through the questionnaire truly meets the need of the research work. Before the publication of the survey, the questionnaire had been reviewed by several academic staff from the research group and people from the industry. This led the questionnaire to a more professional and formal version, with improved content-validity [48]. The appropriate and required number of responses were obtained, some via e-mail as clarification points, and most of them in the evaluation form attached, with the signature and official stamp as depicted for two typical cases in Appendix A.

4.3. Feasibility of the Study

BIM produces a green and effective development-approach that is actively demanded by any industry, to enhance progress and overall performance by decreasing cost and budget issues, time issues, data-loss concerns, and the challenges associated with behind-schedule job offers [49]. However, as the BIM model matures, it is always used to optimise a particular process or stage. The information model becomes a vital element of decision-making throughout the asset’s design, construction, and management phases; incorporating this knowledge and data into the BIM process requires a defined strategy [50]. The BIM collaboration platform establishes a centralized environment in which architects, engineers, contractors, clients, and other construction-team members may access and communicate with the BIM model [51,52]. BIM is rapidly gaining favour as a collaborative method for planning and building structures [53].

BIM methodology is currently being developed, and is being utilised mostly for new construction projects [32]. Additionally, BIM is a well-established emerging technology in the architectural, engineering, and construction (AEC) fields. Given a set of operational limitations, the building-retrofit optimization task is to identify, develop, and implement the most cost-effective retrofit solutions to improve energy performance while maintaining adequate service levels and acceptable interior-thermal comfort. Existing structures consume the greatest energy in the building sector, while new construction replaces around 1.0–3.0% of existing structures each year [33].

5. Methods of Assessment

In this section, the methods of analysis are described, starting with the demography of the respondents and the descriptions of the relevant businesses.

5.1. Respondents’ Background Information

Table 3. Statistics of demography.

Respondents’ Specialization			Construction Sector
Project Manager	6	20%	Public	10	33%
Contactor	5	17%	Private	20	67%
Architect	2	7%	Total	30	100%
Engineer	14	46%
Consultant	3	10%	Qualification
Total	30	100%	Diploma	2	7%
			BSc	11	36%
Age			MSc	14	47%
25–30 Years	4	13%	PhD	3	10%
31–40 Years	13	43%	Total	30	100%
41–50 Years	11	37%
Above 50 Years	2	7%	Professional Field
Total	30	100%	Architect	3	10%
			Construction Manager	10	34%
Years of Experience			Civil Engineer	7	23%
fewer than 5 years	5	17%	BIM Expert	6	20%
5 to 10 years	11	36%	Builder	4	13%
10 to 15 years	6	20%	Total	30	100%
15 to 20 years	3	10%
More than 20 years	5	17%	Training in BIM
Total	30	100%	Yes	19	63%
			No	11	37%
Company Business			Total	30	100
Construction	15	50%
Design	4	13%
Multidiscipline	11	37%
Total	30	100%

shows the demography of the respondents, and includes the specialization, age, years of experience, type and sector of the business, qualification of the respondent, professional field, level of BIM awareness, and the training in BIM.

5.2. The Validity of the Internal Consistency and Stability of the Tool

The probability or representative sampling-method was employed as the sampling method. Probability sampling infers that the units from the population were selected randomly [54]. The probability-sampling procedure can be categorised into four phases: detect a suitable sampling frame, based on the research question(s) and objectives; adopt an appropriate sample size; select the most appropriate technique and sample, and check that the model is representative of the population. After obtaining responses and opinions from experts, the questionnaire was developed and revised, with 85 statements.

5.3. Testing for Instrument Reliability and Validity

A questionnaire can be reliable and valid if Cronbach α is more significant than 0.7. The reliability test was conducted on the questionnaire used in the pilot study.

Table 4. Reliability of questionnaire dimensions.

Dimension	Code	Cronbach Alpha
Energy Status of BIM Retro Public Buildings
Driver	ED01-04	0.791
Utilization	ET01-04	0.830
Barriers	EB01-06	0.865
Expectation	EE01-04	0.845
Strategies for Analyzing Energy Consumption
Benefits	SB01-03	0.808
Obstacles	SO01-06	0.857
Sustainability	SS01-03	0.823
Consideration	SC01-06	0.805
Heat Reduction	SR01-05	0.831
Energy Saving	SE01-05	0.859
Construction of Framework
Barriers	CB01-06	0.912
Social	CS01-04	0.820
Economy	CE01-03	0.728
Regulation	CR01-05	0.875
Psychology	CP01-06	0.904
Managerial	CM01-07	0.915
Technical	CT01-09	0.933
		0.987

shows the results of the reliability of the whole questionnaire in the pilot study.

5.4. Reliability of All Constructs

To measure the stability of the study tool (the questionnaire) the researcher used Cronbach’s alpha to ensure the stability of the study tool on a pilot sample from (30).

Table 5. Reliability of the three parts of the questionnaire.

Construct	Number of Statements	Cronbach Reliability
Energy Status	18	0.942
Strategies	27	0.957
Framework Construction	40	0.979
TOTAL	85	0.987

shows the Cronbach reliability of the three main constructs of energy status, strategies, and framework construction.

It is clear from the table that the general stability-coefficient of the constructs is high, reaching (0.987) for the total of the eighty-five resolution statements, while the stability of each construct ranged between 0.942 (minimum) and 0.979 (maximum). These results indicate that the questionnaire has a significant degree of reliability that can be relied upon in the field application of the study, according to the Nalny scale, which was adopted as a minimum of 0.70 for stability. The highest reliability occurred for the framework construction, while the lowest amongst the three was found to the first construct of the energy status. It seems that the statements of the last construct were easy for the respondents to comprehend, as these statements avoided using technical terms as in the first and second construct. Statistically, the average for the energy-status construct and the framework construction is 3.25 and 3.29, respectively, while the standard deviation is 0.97 and 1.01, respectively. This means that the range of answers in the third construct is slightly higher than its corresponding range in construct 1.

5.5. Internal Consistency Validity

5.5.1. First Construct

Table 6. Validation of the internal consistency (first construct).

Name of Phrases	Correlation Coefficient	Sig. (2-Tailed)	Name of Phrases	Correlation Coefficient	Sig. (2-Tailed)
ED01	0.716 **	0.000	EB01	0.814 **	0.000
ED02	0.718 **	0.000	EB02	0.803 **	0.000
ED03	0.649 **	0.000	EB03	0.616 **	0.000
ED04	0.709 **	0.000	EB04	0.690 **	0.000
ET01	0.640 **	0.000	EB05	0.678 **	0.000
ET02	0.912 **	0.000	EB06	0.643 **	0.000
ET03	0.722 **	0.000	EE01	0.774 **	0.000
ET04	0.610 **	0.000	EE02	0.763 **	0.000
			EE03	0.710 **	0.000
			EE04	0.628 **	0.000

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

shows that the internal consistency of the questionnaire was verified by calculating the Pearson correlation coefficient between the statements of each of the four constructs and the total score for the construct to which the statement belongs, using the (SPSS) Statistical Package. The first verification was carried out on the first construct “Energy Status of BIM Retrofitting in Public Buildings”, as depicted in Table 4, with n = 30. Referring to Table 2, the first construct consists of four dimensions, with 18 statements. All correlation coefficients were calculated at p of 5%; however, the two-tailed results were statistically significant at much lower than the p of 5%. However, the correlation with the highest value of 0.912 belonged to ET02 ($p$ much smaller than 1) and 0.610 ($p$ much smaller than 1) at 0.610. The raw data of the responses showed that ET02 had a higher average than ET04 (3.4, compared with 3.0). This result means that there was a better understanding of ET02 (the design-development phase), compared with ET04 (post-construction maintenance), which is subjected to the record and memory available.

5.5.2. Second Construct

The second verification was carried out on the second construct “Strategies for Analysing Energy Consumption”, as depicted in Table 5, with n = 30. Referring to Table 2, the second construct consists of four dimensions, with 27 statements. All correlation coefficients were calculated at p of 5%; however, the two-tailed results were statistically significant at much lower than the $p$ of 5%. However, the correlation with the highest value of 0.884 belonged to SR01 ($p$ much smaller than 1) and SR05 ($p$ much smaller than 1) at 0.451 as explained in

Table 7. Validation of the internal consistency (second construct).

Name of Phrases	Correlation Coefficient	Sig. (2-Tailed)	Name of Phrases	Correlation Coefficient	Sig. (2-Tailed)
SO01	0.524 **	0.003	SC01	0.642 **	0.000
SO02	0.786 **	0.000	SC02	0.458 *	0.011
SO03	0.722 **	0.000	SC03	0.654 **	0.000
SA01	0.717 **	0.000	SC04	0.526 **	0.003
SA02	0.868 **	0.000	SC05	0.634 **	0.000
SA03	0.733 **	0.000	SR01	0.884 **	0.000
SA04	0.507 **	0.004	SR02	0.806 **	0.000
SA05	0.796 **	0.000	SR03	0.763 **	0.000
SA06	0.584 **	0.001	SR04	0.568 **	0.001
SS01	0.675 **	0.000	SR05	0.451 *	0.012
SS02	0.815 **	0.000	SE01	0.769 **	0.000
SS03	0.710 **	0.000	SE02	0.724 **	0.000
			SE03	0.628 **	0.000
			SE04	0.669 **	0.000
			SE05	0.815 **	0.000

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

. The raw data of the responses showed that SR01 had a higher average than ET04 (3.2,6 compared with 3.03). This result means that there was better understanding of SR01 (using building-fabric, green-insulation roof, wall, etc.), compared with SR05 (establishing a secondary roof), which reflects the awareness of people concerning the environment as being greater than adding a secondary roof.

5.5.3. Third Construct

The third verification was carried out on the third construct “Construction Framework”, as depicted in Table 6, with n = 30. Referring to Table 2, the third construct consists of four dimensions, with 27 statements. All correlation coefficients were calculated at p of 5%; however, the two-tailed results were statistically significant at much lower than the p of 5%. However, the correlation with the highest value of 0.879 belonged to CM01 (p much smaller than 1) and CR05 (p much smaller than 1), at 0.451. The raw data of the responses showed that DR01 had a higher average than ET04 (3.33, compared with 3.23). This result means that there was better understanding of CM01 (better management of project requirements and capacity), compared with CR05 (organizational structure). The dependence of both items is on the ability of the respondent to just work on managing or organizing the project structure; however, organizing the structure is only a part of the management, which makes it difficult for the respondent to reach an appropriate solution. The details are shown in

Table 8. Validation of the internal consistency (third construct).

Name of Phrases	Correlation Coefficient	Sig. (2-Tailed)	Name of Phrases	Correlation Coefficient	Sig. (2-Tailed)
CB01	0.801 **	0.000	CP04	0.681 **	0.000
CB02	0.689 **	0.000	CP05	0.715 **	0.000
CB03	0.666 **	0.000	CP06	0.821 **	0.000
CB04	0.800 **	0.000	CM01	0.879 **	0.000
CB05	0.746 **	0.000	CM02	0.848 **	0.000
CB06	0.811 **	0.000	CM03	0.866 **	0.000
CS01	0.784 **	0.000	CM04	0.697 **	0.000
CS02	0.662 **	0.000	CM05	0.756 **	0.000
CS03	0.655 **	0.000	CM06	0.702 **	0.000
CS04	0.767 **	0.000	CM07	0.682 **	0.000
CE01	0.774 **	0.000	CT01	0.758 **	0.000
CE02	0.762 **	0.000	CT02	0.766 **	0.000
CE03	0.715 **	0.000	CT03	0.620 **	0.000
CR01	0.803 **	0.000	CT04	0.756 **	0.000
CR02	0.781 **	0.000	CT05	0.814 **	0.000
CR03	0.670 **	0.000	CT06	0.821 **	0.000
CR04	0.792 **	0.000	CT07	0.660 **	0.000
CR05	0.607 **	0.000	CT08	0.619 **	0.000
CP01	0.782 **	0.000	CT09	0.781 **	0.000
CP02	0.777 **	0.000
CP03	0.748 **	0.000

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

.

5.6. Pearson’s Correlation

Pearson’s correlation analysis was used in this study [55]. This correlation is represented by a single number that establishes a relationship between two variables that are related in a linear relationship. Pearson correlation ranges between −1 and +1, with 0 indicating no correlation. The positive and negative correlation describe a direct and inverse relationship between the two variables, respectively. There are two types of correlation: (1) positive, where the two variables are increasing or decreasing together, and (2) negative, where the two variables are opposing each other. The correlation coefficient ranges between −1 and +1, where the highest positive correlation appears at +1, the highest negative correlation appears at −1, and a correlation of zero means there is no effect. The strength of the correlation was identified based on the following Pearson statistical classification: 0.00–0.19 (very weak); 0.20–0.39 (weak); 0.40–0.59 (moderate); 060–0.79 (strong); and 0.80–1.00 (very strong). Based on these suggested values, the correlation values considered here are between 0.30 and 1.00 [56].

Table 9. Pearson’s correlation between all factors.

	ED	ET	EB	EE	SB	SO	SS	SC	SR	SE	CB	CS	CE	CR	CP	CM	CT
ED	1
ET	0.787	1
EB	0.744	0.701	1
EE	0.685	0.732	0.723	1
SB	0.607	0.613	0.703	0.769	1
SO	0.723	0.809	0.729	0.816	0.730	1
SS	0.740	0.738	0.604	0.681	0.663	0.747	1
SC	0.752	0.729	0.534	0.620	0.464	0.686	0.625	1
SR	0.839	0.671	0.755	0.749	0.611	0.774	0.818	0.653	1
SE	0.751	0.677	0.771	0.831	0.759	0.749	0.664	0.608	0.825	1
CB	0.501	0.642	0.633	0.618	0.592	0.811	0.690	0.622	0.608	0.536	1
CS	0.651	0.657	0.712	0.669	0.767	0.704	0.699	0.553	0.656	0.677	0.785	1
CE	0.702	0.828	0.794	0.691	0.599	0.925	0.759	0.728	0.739	0.682	0.883	0.786	1
CR	0.839	0.839	0.749	0.693	0.555	0.803	0.779	0.739	0.838	0.721	0.773	0.764	0.836	1
CP	0.785	0.710	0.713	0.676	0.681	0.870	0.723	0.626	0.803	0.746	0.760	0.818	0.857	0.863	1
CM	0.725	0.731	0.685	0.818	0.773	0.944	0.743	0.708	0.772	0.775	0.821	0.802	0.890	0.796	0.855	1
CT	0.667	0.711	0.781	0.764	0.809	0.768	0.626	0.684	0.653	0.746	0.754	0.768	0.770	0.713	0.748	0.854	1

shows the Pearson’s correlation between all seventeen factors that belong to the three constructs. This test attempts to find whether each factor is either positively, negatively, or not correlated with any two factors. The results show that the correlation is positive, which means that any factor could enhance all other factors. However, this enhancement is not at the same strength. The results show that the highest correlation occurred between CM (management) and CE (economy) (both belong to the strategies dimension) at 0.890, while the lowest-strength correlation occurred between SC (consideration) and SB (benefit), at 0.464. The highest correlation between CM and CE could be attributed to the highest dependency between the management and achieving a better economy. On the other hand, the consideration seemingly shows a very low correlation with benefit, probably because of not enough consideration paid by the respondents. Other than the extreme cases, the correlation between any two factors is positive and greater than 0.3, which is the lowest accepted value.

This result agrees partly with the study results in [57], which dealt with (a pilot study investigation of the current practices and the feasibility of BIM implementation in Algerian AEC industry). The study aimed to investigate current practices and the feasibility of applying BIM in the Algerian AEC industry. The study showed that the survey results have consistent and reliable content that leads to further study with a sample of architects, engineers, and contractors.

Based on the results of the Spearman correlation test to measure the internal consistency between the statements and the dimension, the results of the study showed a positive correlation between each item and the higher axis. The highest correlation was between statement (Y3_6) (a regulatory framework clarifying rights, responsibilities, and obligations), and its main axis (best practices for implementing BIM in the Algerian AEC industry) at 0.752, and this indicates the importance of providing a regulatory framework for clarifying rights and obligations during the implementation of BIM in the construction industry in Algeria, from the respondent’ point of view. Meanwhile, there was a lower correlation between clause (Y2_3) (we lack demand from our customers for BIM adoption), with its main axis (BIM implementation challenges in the Algerian AEC industry) at 0.409, indicating the respondents’ lack of interest in focusing on customers’ demand for BIM adoption in the Algerian construction industry. These results differ from the current study, where the correlation coefficient (CS01) (The social effect such as client demand and contracts) reaches (0.784), which indicates the importance of social factors for BIM retrofitting projects.

6. Data Ethics

Data has become an integral part of people’s everyday lives, and a component that fosters social growth, creating changes in social-economic development, social structure, and lifestyle. Big data processing and analysis may simplify, organize, and exploit difficult-to-collect data. Its technology has expanded information gathering. It can rapidly and correctly retrieve important information from a complicated and large database, to aid decision-makers.

As a traditional industry of the national economy, building cannot count on quick growth in the current age. Information technology must be leveraged to transform, improve, and advance the construction industry, as it continues to grow. BIM technology’s rapid growth in engineering construction has improved large-scale design, construction, operation, and maintenance technology. BIM can improve construction projects and enhance the industry. Long construction-periods, complicated composition, high mobility, and multiple high-altitude activities contribute to accidents in the construction industry. Engineering ethics in Malaysia are still at the beginning. It is frequently neglected or undervalued, lacks the necessary expertise, and has not been the driving force and operational mechanism for engineering ethical norms. Therefore, technical growth and development which are vital for engineering ethics in civil engineering, bring new challenges.

Engineering in construction is a significant indicator of societal progress. It is a representation of both scientific and technical advancement, as well as urban economic growth. It always has an impact on human survival and advancement. The effectiveness of civil engineering projects affects social, political, and economic activity significantly, in addition to the protection of individuals and their personal property. Construction is increasing, but its difficulties are growing. Firstly, the construction industry has long-standing problems with safety, quality, and cleanliness. Secondly, as a conventional industry, construction lags behind in innovation and the application of new technologies, with solidification thinking and inadequate promotion and breadth of new technologies. On one hand, the systems and mechanisms in the construction industry need to be reformed and improved; on the other hand, the engineering ethics of civil engineering is also a factor that cannot be ignored, so strengthening the construction of civil engineering is also important for promoting the high-quality development of the construction industry initiatives.

7. The Study Contribution

As the globe faces global warming and climate-change difficulties, the cause of the problems is a massive rise in energy use and greenhouse gas (GHG) emissions from the current building stock [58]. Retrofitting existing buildings to reduce energy use is the best alternative strategy. Retrofitting highlights numerous components that contribute to excessive energy usage and those features examined by green consultants during the retrofit. Hence, this research could be helpful for building owners, occupants, and green consultants who wish to retrofit existing buildings.

Adopting BIM for retrofitting existing buildings is an important research issue, especially in Malaysia [32]. Retrofitting existing buildings may significantly contribute to construction sustainability regarding energy performance [10]. The present research solves energy-related difficulties by leveraging BIM as a retrofit tool for public-building energy efficiency.

For the choice of public buildings, two elements are mentioned. The first reason is that government entities work on a tight timetable. Therefore, public facilities are underutilised. The second reason is that these buildings were built approximately three decades ago, and have since undergone repair and maintenance, especially in energy-related concerns such as air conditioning (A/C), which uses the most energy in most public buildings. As a consequence, retrofitting may improve the building’s energy efficiency. Furthermore, BIM retrofitting offers sustainable routes in all three areas of sustainability: environmental, economic, and social. Green retrofitting is more beneficial in certain aspects than demolishing and rebuilding outdated buildings [59].

This research aims to reduce energy usage while complying with BIM standards and regulations. Modifying existing structures is one approach to achieving construction sustainability, by increasing energy-efficiency and enhancing the building’s environmental performance or lowering energy-usage. It will also help the expansion of the literature on upgrading existing public buildings. It also assists in obtaining fresh insights into the environmental consequences of different construction techniques on the overall energy-efficiency needs of the building and in realising the crucial significance of energy sustainability for existing buildings.

It is connected to the electrical distribution system, as well. Sustainable retrofits benefit the environment by increasing the structure’s three-dimensional value; economically, by increasing rental revenue, cutting running expenses, and prolonging the facility’s life, and socially, through encouraging general health and well-being, and increasing productivity. In terms of the environment, the benefit is through lowering carbon dioxide emissions. The proposed framework would undoubtedly reduce energy usage in public buildings, by utilising BIM as a retrofit tool for future design, construction, operation, and maintenance.

8. Conclusions

Numerous active research-initiatives investigate using BIM platforms to enhance design and construction processes. BIM-based tools such as Autodesk Revit’s Architecture can handle multiple data-input types that deal with 3-D design, energy models, schedules, and cost estimates. These tools offer rigorous simulation and visualisation options in an integrated manner, enabling engineers and contractors to track and control projects effectively. This study investigates the reliability and validity of a constructed questionnaire to pre-determine the logic behind constructing such a questionnaire which will be used in a large-scale study and the relevant analysis. The literature has highlighted the connection between BIM and energy-driven retrofits. However, the application of BIM to the retrofitting of existing structures confronts obstacles, which may be attributable to the multidisciplinary character of information sharing, the timeliness of communication, and the large number of technological components required to provide an optimal exchange.

The pilot-study sample was determined based on the number of questionnaires gathered to test the parameters. The number of questionnaires was 167, and out of these only 30 were randomly chosen. SPSS was used for estimating the percentages of the demographic attributes for the respondents, the face validity, internal-consistency validity, the validation of all contracts, and Pearson’s correlation.

The results show that engineers constitute 46%, project managers (20%), contractors (17%), and the rest (approximately 17%) are divided among other professionals. The validity of internal consistency ranges from 0.791 to 0.912, which reflects perfect consistency. The internal consistency of each part was recorded at 0.942 (energy), 0.957 (strategies), and 0.979 (framework). The validation for the energy part ranges from 0.610 to 0.912; for strategies, (0.451 to 0.884) and for the framework (0.681 to 0.884). Pearson’s correlation for all 17 questions shows a minimum value of 0.464, while the maximum value is 0.890.

The results show that all questionnaire elements (Table 2) were successfully validated with mainly an alpha Cronbach factor higher than 0.6—the threshold accepted by most researchers. Hence, the work on the broader scale testing and analysis could proceed.

The limitations of the research can be seen in collecting enough data within a short time, because of the COVID-19 pandemic. The other limitation came from the nature of the research, as not many companies are involved in retrofitting.