Boosting large‐scale construction project risk management: Application of the impact of building information modeling, knowledge management, and sustainable practices for optimal productivity

This study investigates the relationship between Building Information Modeling (BIM) and knowledge management in large‐scale construction projects, emphasizing risk management, and increasing productivity. This study aims to identify the most important and impactful aspects of modern management techniques in the large‐scale construction sector to improve construction quality; focusing on cost reduction, energy conservation, and pollution reduction during construction. Employing a validated questionnaire from experts and the Analytical Hierarchy Process (AHP), the research prioritizes key factors, elucidating the pivotal role of BIM in understanding project geometry, efficient documentation, design flexibility, and reduced project delays. Ascertained through data analysis employing the pairwise comparisons method of the AHP analysis method, the compatibility of all comparisons and matrices arising from the decision‐making process of experts was below 0.1. The results establish BIM's technical capabilities, economic considerations, and pollution reduction as paramount, contributing significantly to improve large‐scale construction project risk management. The prioritization of the main criteria reveals their significance in the following order: BIM technical capabilities (0.281), BIM economic capabilities (0.231), pollution reduction (0.228), knowledge management capabilities (0.096), BIM time capabilities (0.090), and human resources management capabilities (0.074). BIM can improve risk management for large‐scale construction projects by: using the BIM model to understand the geometry and features of large‐scale construction projects; digitizing and organizing contract documents, project data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis, and other project‐related documents for efficient project management; decreased duplication and project delays; reducing the need for on‐site personnel during implementation and CO2 emissions reduction. According to the results of the BIM model utilization to better understand the geometry and characteristics of large‐scale construction projects, the item was identified as the most significant and influential among the main components.


| INTRODUCTION
2][3] One of the significant outcomes is the rise in energy consumption and demand.Urban areas are the primary consumers of energy. 4,5Over 50% of the global population resides in urban areas, and this proportion is progressively growing. 6,7Urban energy consumption is highly concentrated, with around two-thirds of global primary energy being used by cities. 8,9 This trend is expected to persist in both developed and emerging nations.][12][13][14] The energy issue holds significant relevance and is essential from a global standpoint.Given the limited resources and the need to conserve them, it has emerged as a major concern in the modern era. 15There is a growing shift towards environmental protection as people recognize the need to efficiently utilize natural resources. 16,17This emerging trend has been accomplished through the creation of sustainable structures that fulfill the requirements of the current generation while safeguarding the capacity of future generations to fulfill their own demands. 180][21][22] Conversely, sustainability refers to the utilization and harmonization of the environment and architectural structures while also improving the management of the ecosystem. 23,246][27] Energy efficiency has emerged as a crucial policy instrument worldwide to address the substantial increase in energy use. 28Furthermore, empirical evidence suggests that there is a lack of comprehensive research on energy efficiency, specifically at the urban level.
The adoption of appropriate decision-making methodologies is crucial in addressing the multifaceted challenges of large-scale construction projects, particularly in the realms of risk management, carbon emission mitigation, and overall productivity enhancement.In this context, the Analytical Hierarchy Process (AHP) emerges as a well-established and robust method that aligns seamlessly with the intricate dynamics of such complex endeavors. 29AHP, a decision-making tool rooted in mathematical modeling, has demonstrated remarkable efficacy in handling problems characterized by diverse criteria, competing objectives, and the involvement of multiple stakeholders.Its structured framework provides a systematic approach to evaluating and prioritizing various factors, offering a quantitative basis for decisionmaking.It categorizes decision-making elements into various dimensions, systematically decomposing and organizing problems from multiple perspectives.This hierarchical approach breaks down large and complex issues into smaller, more manageable subproblems, enabling the individual assessment of these components.Such a process streamlines the decision-making procedure, particularly for intricate problems. 30he utilization of cutting-edge technologies like Building Information Modeling (BIM) and knowledge management has not received adequate attention, especially concerning their role in enhancing risk management, particularly in large-scale construction projects.This study aims to comprehensively investigate the relationship between BIM, knowledge management, and risk management in the context of large construction projects.From technical aspects to environmental considerations, including greenhouse gas emissions, various facets of this relationship have been extensively explored.Through the application of the AHP, this research seeks to elucidate the specific contributions of BIM and knowledge management to the enhancement of risk management in large construction projects and identify the critical criteria pivotal to this domain.The main objective of this study is to identify the crucial and impactful components of contemporary management techniques in the extensive construction sector and rank them in terms of priority to enhance the quality of construction.This will be achieved by adopting an approach focusing on cost reduction, energy conservation, and minimizing pollutants during construction.

| LITERATURE REVIEW
Projects in the construction industry are carefully defined and planned in terms of both time and space.However, due to the dynamic nature of both components, uncertainty and risk are inherent in all projects.It is possible that it faces both threats and opportunities.Risk is an inherent part of every endeavor, and it is impractical to completely eliminate it.The impact of risk on project objectives can be mitigated by implementing knowledge management, risk assessment, and management strategies.Risk management is a methodical procedure for identifying, analyzing, and responding to potential project risks.Its goal is to maximize positive outcomes while reducing the likelihood or severity of negative impacts on project objectives.A comprehensive collection that considers time, cost, quality, productivity, and other relevant factors.
The BIM System is a novel technology that uses computer modeling to facilitate various stages of simulation, planning, design, construction, and execution in construction projects.The designated apparatus for utilization.][33][34] Many construction companies around the world have made recent investments in knowledge management.Despite some companies' success in implementing knowledge management, the failure rate remains high.A variety of conditions, contexts, and experiences influence the success or failure of an organization's knowledge management.The overall situation of the organization and the project in the field of knowledge management, also known as the degree of knowledge management maturity, plays an important role here. 35efore investing in this high-risk area, businesses should use a tool to reduce the uncertainty of implementing knowledge management initiatives.In other words, evaluating the company's current situation in the field of knowledge management and conducting an in-depth analysis of the maturity level of knowledge management are crucial. 36This type of investment is extremely risky due to the lack of appropriate mechanisms for assessing the current state of the company to implement knowledge management.To target the use of knowledge as a tool to create a competitive advantage and organize the development stages of knowledge management in the organization, it is functionally important to determine the maturity level of knowledge management and the factors that influence project managers' decisionmaking to apply and improve knowledge management. 37he implementation of knowledge management in construction companies results in shared comprehension, thereby enhancing organizational performance and generating a competitive advantage. 38,39It is also effective at reducing costs due to the dispersion of material and human capital, avoiding duplication of effort, enhancing decision-making, and adjusting to change. 40The construction industry produces a very large, expensive, custommade product.This industry is extremely knowledgeintensive, and its performance depends on knowledge input.In recent years, the concept of knowledge management, which saves money and time, has been brought up repeatedly in this industry, resulting in greater employer satisfaction, improved scheduling and cost control, and innovation. 41,42Companies active in the construction industry invest significantly in knowledge management to reduce project management costs and reap the benefits of economies of scale.It appears that civil engineering and construction companies will continue to utilize knowledge management in the future. 43,44aturity refers to a specific process for managing, evaluating, and controlling project activities.In practice, maturity models are viewed as tools for attaining knowledge management maturity and implementing the appropriate strategy.The knowledge management maturity model allows businesses and organizations to conduct a comprehensive assessment of knowledge management activities and understand the current state of knowledge management in a systematic manner.More specifically, it allows them to identify and overcome obstacles, as well as have a logical evaluation basis and obtain the resources required for the next level of maturity.Using maturity models is critical for knowledge management for a variety of reasons. 45

| RESEARCH METHODOLOGY
In this study, a comprehensive research methodology was employed to assess the intricate relationship between BIM, knowledge management, and their impact on risk management, and productivity enhancement in large-scale construction projects.It specifically focuses on content analysis connected to risk in building projects.Information regarding the theoretical premise of research and the identification of the most important capabilities of BIM for the promotion of risk management in large construction projects will be gathered through library measures.In addition, field measures, such as interviews with relevant specialists and experts and the development of questionnaires to capture the opinions of construction industry experts, were employed.
In describing the method and how to analyze the data in this research, it should be noted that to break down and analyze the data, the concept of risk and risk management was examined and evaluated in the first stage, and BIM capabilities were examined through library research in the second stage.In addition, knowledge management is identified as an improvement in risk management in large-scale construction.To ensure the transparency, reliability, and repeatability of the research findings, a multiple-criteria decision-making approach, particularly utilizing the AHP, was adopted.Multiple-criteria decision-making provided a structured methodology for systematically analyzing and prioritizing diverse criteria, essential for the multidimensional study.AHP, as a quantitative decision-making tool, allowed for the synthesis of expert opinions, contributing to a more objective evaluation.
In light of the purpose of the present study, which is to evaluate the role of BIM and knowledge management in enhancing risk management in large construction projects in the construction industry, the investigated variables include dependent variables (improvement in risk management).variables that are independent (BIM, knowledge management, and risk).
The statistical population in the qualitative segment consists of senior and middle construction industry managers with at least a master's degree and 15 years of experience.The recommended sample size for qualitative methods and interview-based studies is between 5 and 25 participants.In addition, nonprobability and targeted sampling methods are preferable for the qualitative portion.Targeted sampling was used for the qualitative portion of this study, and with 19 interviews, theoretical saturation was reached.In the second section, the viewpoints of construction industry managers and experts were utilized to examine the status of BIM application and the maturity of knowledge management in promoting risk management.Cochran's formula predicted a minimum sample size of 132 individuals based on the total size of the statistical population, which is approximately 200 individuals.For greater certainty, a sample of 140 individuals was chosen.
In the questionnaire for this study, after a general introduction to the topic, the respondent was asked to provide information about his demographics, including his age, experience, and job title.Moreover, in the second section, the respondents were asked to compare the capabilities of BIM and knowledge management in promoting risk management in large construction projects in the construction industry by presenting the most important capabilities of BIM and knowledge management.

| Multiple-criteria decision-making
The types of decision-making concerns are presented as criteria.The purpose of comparing criteria is to establish the relative weight of each in determining the best answer.After converting qualitative indicators into numbers and determining their weight in decision-making, the best option is then selected based on its score relative to other options.The following matrix (Equation 1) can represent the decision-making matrix in multiple-criteria decisionmaking problems. (1) In this decision matrix, A i represents the ith option, and x j represents the jth criteria.It was used to determine the desirability of each option.][48] The AHP procedure is a highly comprehensive methodology specifically developed for decision-making, including several variables.This approach involves the examination and visual representation of intricate problems in the structure of a hierarchy.Hierarchy is an effective tool for decision-makers who are able to maintain a comprehensive perspective on the issue while also paying attention to the specific nuances.This technique enables the systematic incorporation of both quantitative and qualitative aspects into the decisionmaking model.The AHP employs the method of paired comparisons to facilitate selection.This entails evaluating multiple options by comparing them in pairs based on predetermined criteria.By assigning weights to the criteria and utilizing units, the preference of each option over the other can be determined for each criterion.][51] In AHP, the initial stage involves categorizing the problem under consideration.The highest level consists of the overall objective of the problem, while the subsequent level includes the criteria and solutions.There is no limit to the number of levels in a hierarchy.During the modeling phase, the problem and decision-making objective are expressed as a series of interconnected decision elements.Decision elements consist of "decision options" and "decision criteria."AHP must divide a problem with multiple criteria into multiple levels.The high level is the primary objective of the decision-making process.The second level displays the most important and main criteria, (which may be broken down into subcriteria and made more specific in the third level).Figure 1 shows the structure of an AHP with three main criteria and seven subcriteria.
To determine the importance of criteria and subcriteria, all criteria and subcriteria are compared using a pairwise comparison matrix, and then the weights of each criterion and subcriterion are determined using a variety of techniques.To proceed, it is necessary to compute the absolute or final weight of each option.This can be achieved by multiplying the weight of each criterion by the weight of the corresponding option in relation to that criterion.Lastly, the priority of the options is determined by obtaining their absolute weight.Typically, a pairwise comparison matrix represents the relationship between elements, with a ij denoting the preference of the ith element over the jth element (Equation 2).(2) The calculations involved in the rank-order analysis process rely on the initial judgment of the decisionmaker, which is represented by the pairwise comparison matrix.Any mistakes or inconsistencies in the comparison and assessment of importance between options can affect the results.The options and indicators skew the final outcome derived from the calculations.The Incompatibility Rate (IR) is a tool that assesses the compatibility and reliability of priorities derived from comparisons.It is calculated using a specific method.The following steps are employed to calculate IR: Step 1. Compute the weighted sum vector (WSV): By multiplying the matrix of pairwise comparisons by the "relative weight" column vector, which is WSV. 52tep 2. Compute the compatibility vector (CV): By dividing the elements of the total weighted vector by the relative priority vector, which yields the CV. 53tep 3. Obtaining λ max : λ max corresponds to the average of the CV's elements.
Step 4. Calculation of the compatibility index (CI): The CI is defined by Equation (3): where n is the number of options in the problem. 54tep 5. Computation of the consistency ratio (CR): the mechanism that is considered to check the inconsistency in the judgments, a coefficient calculation called the CR, which is the result of dividing the inconsistency index by the randomness index (Equation 4).
If this coefficient is less than or equal to 0.1, the agreement in the judgments is accepted; otherwise, the judgments must be reconsidered. 55sing the judgments of a single manager to form the matrix of pairwise comparisons, which is the basis for decision-making when applying AHP to organizational issues, cannot result in a highly accurate decision matrix.Consequently, group cooperation and thought are logical and comprehensive methods for an organization's decision-making system.The best method for combining judgments in AHP group decision-making is the geometric mean.Assume that a ij (k) is the component associated with person k to compare criteria i with criteria j (Equation 5).
When people's opinions have varying weight in their judgments in a matrix of pairwise comparisons, a weight (W) can be considered for each person's opinion and Equation ( 6) can be applied.
where n is equal to Equation ( 7):

| Conceptual model of research
The study integrated the concepts of BIM and knowledge management to synergistically enhance risk management and productivity in large-scale construction projects.BIM's technical capabilities were harnessed for tasks such as understanding project geometry, identifying interferences, utilizing risk management approaches, and making informed decisions throughout different project phases.Knowledge management played a crucial role in transferring design data, documenting contracts, and facilitating effective communication among project stakeholders.Key parameters included in the research methodology were BIM technical capabilities, knowledge management capabilities, BIM economic capabilities, BIM time capabilities, human resources management capabilities, and pollution reduction capabilities.The methodology placed specific emphasis on productivity enhancement, encompassing design flexibility to manage changes efficiently, reducing project delays through accurate time estimation, and optimizing human resources management for effective project coordination.Each parameter was meticulously examined using the AHP to determine its significance in contributing to increased productivity and reduced risks.

| RESULTS AND DISCUSSION
This section presents a detailed analysis and breakdown of the data and introduces the main factors associated with the capabilities of BIM and knowledge management in enhancing the risk management of large-scale construction projects.Initially, an assessment of the questionnaire results is conducted.The subsequent section examines the examination and evaluation of the data, as well as the ranking of the identified capabilities using the AHP.

| Reliability and validity of the questionnaire
The parameters presented in this study cover a wide range of variables in the field of BIM capabilities and knowledge management for reducing risk in large construction projects.As a result, the statistical community should include a variety of specializations, skills, and job fields associated with this stream.As a result, based on the assumptions and limitations, as well as the scope of the current research, the statistical community under investigation in this study consists of 30 specialists, experts, and experts in construction projects who are actively engaged.It is important to note that, due to the lack of widespread access to all members of the statistical community in this study, the sampling method was used.In this study, because the members of the statistical community have almost identical characteristics, a homogeneous statistical community with a limited number was considered, and a sample was also chosen to collect the necessary information.A simple random sampling method was utilized.
To ensure the level and accuracy of research findings, it is imperative to validate the results.Given that the present study was conducted as field research, the subsequent analysis of its findings will rely on the perspectives of experts and specialists in the construction industry.Therefore, the most effective approach to receiving recognition would be to seek endorsement from these knowledgeable individuals.Identifying pertinent outcomes assessing the credibility and dependability of the examined surveys.The task will be completed during the research process.This validation process will address any issues of nonconfirmation and lack of accuracy in the questionnaire's questions by seeking expert opinions.The researcher will then make necessary adjustments based on the feedback received.The research employed the narrative content validity method.Content validity pertains to the degree to which the selected questions in an examination accurately represent the complete range of possible questions that can be derived from the specific content or subject matter under consideration (Equation 8).

( )
where N is the number of specialists and n e is the number of people who considered that factor necessary.The internal reliability of the test was assessed using Cronbach's alpha method, which confirmed the reliability of the questions.A high Cronbach's alpha rate in a research design indicates that the question design has a high level of reliability.The calculation of Cronbach's alpha coefficient can be performed using Equation (8).
where r α is the reliability coefficient of the entire test, k is the total number of test questions, σ J 2 is the variance of the jth question's score, and σ is the variance of all questions' scores.In addition, if the alpha coefficient is greater than 0.70, the reliability is acceptable.The results of the questionnaire's validity and reliability were 0.91 and 0.87, respectively.Both the level of validity and reliability of the questionnaire exceed the minimum acceptable value, and both validity and reliability are high.

| Data analysis
According to the research model presented in Section 2, after final confirmation of the effective factors and subfactors in enhancing the risk management of large construction projects using BIM capabilities and knowledge management, and based on the evaluations and validation of experts, the final factors and subfactors are provided in Table 1.

| Preference of subcriteria BIM technical capabilities
Table 2 displays the results of the decision-making matrix derived from pairwise comparisons of the subcriteria associated with the first main criteria, that is, the technical and technical capabilities of BIM in enhancing the risk management of large-scale projects.The results indicate that there is sufficient compatibility between the pairwise comparisons of all the subcriteria in this category (the value of the IR is less than 0.1), and it can be concluded that the numerical validity of the comparison results is reliable.
Moreover, in Figure 2A, the results pertaining to the normalized values for determining the degree of superiority of the subcriteria related to the primary technical and technical criteria are presented in relation to one another to determine the more desirable subcriteria.
The prioritization of BIM capabilities in the first main criteria, namely, technical and technical capabilities, according to the degree of preference, are as a3, a5, a1, a2, a4, and a6.

| Knowledge management capabilities
Table 3 displays the results of the decision-making matrix derived from pairwise comparisons of the subcriteria related to the second main criterion, namely, knowledge management capabilities in reducing the risk of largescale projects.
The results indicate that there is sufficient compatibility between the pairwise comparisons of all subcriteria in this category; the IR is less than 0.1.It can be stated that the results obtained from the validity of the number of comparisons are valid.In addition, in Figure 2B, the results pertaining to the normalized values for determining the degree of superiority of the subcriteria related to the main criteria of knowledge management capabilities are presented and compared to identify the more preferable subcriteria.Examining the maturity status of knowledge management in construction companies is necessary to implement the full cycle of knowledge management to increase productivity and create a competitive advantage.
The results obtained from Table 3 and Figure 2C are the prioritization of the knowledge management criteria, according to the preference of b2 and b1, respectively.

| BIM economic capabilities
Table 4 displays the results of the decision-making matrix derived from pairwise comparisons of the subcriteria T A B L E 1 Model main criteria and subcriteria.

Main criteria
Index Subcriteria Index BIM technical capabilities A Beginning with project design bids, creation of integrated multidimensional models to documentation of the project a1 Identifying interferences and structural errors in large construction projects and reporting a2 Using the BIM model to know and better understand the geometry and characteristics of large-scale construction projects a3 Using the risk management approach to evaluate and control risks in different phases of the project life cycle a4 Achieving the correct management decision to adapt to the changes and solve the problems in the different phases of the project by using the digital data available in the BIM maps a5 Convenience in analyzing the complexities of large-scale projects by referring to the exact location of rework or changes created in the design phase of the project using 3D as built sheets a6 Knowledge management capabilities B Transferring the designed data to stakeholders and beneficiaries during different phases of the project's life cycle, in the event that the implementation process is excessively complex, and achieving the correct path and guiding principles for the project's objective b1 Documenting contracts, data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis documents, as well as all other project documents, in the form of a document.To use it in the project, it was digitized.
The following ones b2 BIM economic capabilities C Appropriate estimation of project costs during its life cycle c1 Reducing the costs of changes and rework through the ability to predict different outcomes of project components in the initial design and choose the final design based on those predictions using BIM-based software c2 Design flexibility to reduce, remove, or add the costs of unwanted changes and rework c3 Reducing the costs of reporting and evaluating the project status due to the existence of documented and digital information c4 BIM time capabilities D Reducing the delay in the preparation of project plans d1 Removing the risk of imposing an unreasonable period of time in contracts d2

Reducing duplication of work and reducing project delays d3
Accurate estimation of project time d4 Human resources management capabilities E The noncontinuity of the presence of office resident personnel in the implementation phase and the possibility of control and planning the progress of the project is documented through digital data e1 Managing the wide range of factors involved in a large-scale project e2 Creating coordination between different stakeholders in the initial stages of starting the project design work Reducing material waste and the proper disposal of construction waste f2 Note: Forming the AHP tree requires identifying the parameters and independent and dependent variables.This tree has three levels: the objective, the criteria, and the subcriteria.
| 2291 related to the third main criteria, namely, the economic ability of BIM to reduce the risk of large-scale projects.
The results of the IR value indicate that there is sufficient agreement between the paired comparisons of all subcriteria in this category (the IR value is less than 0.1), so it can be concluded that the results are reliable.
In Figure 2C, the results show how much better each subcriteria is when it comes to the main economic criteria.This helps you figure out which subcriteria is better.related to the main criteria of BIM time capability in enhancing the risk management of large-scale projects.

| BIM time capabilities
The results of the IR value indicate that there is adequate agreement between the pairwise comparisons of all subcriteria within this category (IR value less than 0.1).Meanwhile, Figure 2D displays the results of the superiority of the subcriteria related to the BIM time capability criteria to determine which subcriteria.

| Human resources management capabilities
The results of the decision-making matrix, which was created by comparing the subcriteria related to the main criteria of human resources management capabilities in enhancing risk management for large-scale projects, are shown in Table 6.The IR value results indicate that there is sufficient agreement among the pairwise comparisons of all subcriteria within this category, with an IR value below 0.1.Figure 2E illustrates the outcomes of the subcriteria's superiority in relation to the BIM time capability criteria, aiming to identify the specific subcriteria.

| Pollution reduction
The outcomes of the decision-making matrix, generated through the comparison of subcriteria associated with the primary criteria of pollution reduction capabilities in improving risk management for large-scale projects, are displayed in Table 7.The IR value results demonstrate a high level of consensus among the pairwise comparisons of all subcriteria within this category, as indicated by an IR value below 0.1.Figure 2F demonstrates the results of the subcriteria's superiority compared to the pollution reduction capabilities criteria, with the goal of identifying the specific subcriteria.

| Main criteria preferences comparison
The decision-making matrix findings from pairwise comparisons of the key criteria found in the research based on average first opinions offered by experts as well as based on AHP are shown in Table 8.The comparison of the main elements' rates of consistency with one another's results indicates that the acquired results are suitably trustworthy.Table 9 also includes the findings about the relative levels of preference of the key criteria when compared to one another to identify the key factors.
According to the results of the data analysis, six significant items were identified as the most significant and influential characteristics among the major components defined to give a prioritizing model using the AHP method.There have The following criteria, which are listed in the following order of significance, have been identified as the most crucial and, in reality, the most desired skills of BIM and knowledge management for enhancing the risk management of large-scale construction projects: • Using the BIM model to know and better understand the geometry and characteristics of large-scale construction projects.• Documenting contracts, data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis documents, as well as all other project documents, in the form of a document.To use it in the project, it was digitized.The following ones.Note: On the basis of the weight of the criteria presented in Table 9, the order of priority and significance of the primary criteria is A, C, F, B, D, and E. Three main criteria are listed as follows: • BIM technical capabilities, • BIM economic capabilities, • Pollution reduction.
• Design flexibility to reduce, remove, or add the costs of unwanted changes and rework.• Reducing duplication of work and reducing project delays.
• The noncontinuity of the presence of office resident personnel in the implementation phase and the possibility of control and planning the progress of the project are documented through digital data.• Reducing CO 2 emissions.

| CONCLUSION
This study aimed to assess the impact of BIM and knowledge management on enhancing risk management in large-scale construction projects using AHP.The compatibility of all comparisons and matrices resulting from the decision-making of experts was less than 0.1, as determined by data analysis based on the method of pairwise comparisons using the AHP analysis method.Therefore, it can be stated that the data was accurately evaluated and that all comparison results were reliable.The results of categorizing BIM capabilities for enhancing risk management in large-scale construction projects are presented in the following: • Using the BIM model to know and better understand the geometry and characteristics of large-scale construction projects.• Documenting contracts, data, plans, work instructions, roles and responsibilities, cost, time, and energy analysis documents, as well as all other project documents, in the form of a document.To use it in the project, it was digitized.The following ones.• Design flexibility to reduce, remove, or add the costs of unwanted changes and rework.• Reducing duplication of work and reducing project delays.• The noncontinuity of the presence of office resident personnel in the implementation phase and the possibility of control and planning the progress of the project are documented through digital data.• Reducing CO 2 emissions.Meanwhile, based on the weight of the criteria, the order of priority and significance of the main criteria is A, C, F, B, D, and E. Three main criteria are listed as follows:

F I G U R E 1
Scheme of a typical AHP structure with three main criteria.AHP, Analytical Hierarchy Process.

e3
Reducing conflicts and claims of a wide range of factors involved in a large-scale project e4Pollution reduction F Reducing CO 2 emissions f1

F
I G U R E 2 The superiority of the subcriteria related to the main criteria of (A) BIM technical capabilities, (B) knowledge management capabilities, (C) BIM economic capabilities, (D) BIM time capabilities, (E) human resources management capabilities, and (F) Pollution reduction.BIM, Building Information Modeling.

T A B L E 3 4
The average matrix of pairwise comparisons of subcriteria related to criteria B. The average matrix of pairwise comparisons of subcriteria related to criteria C. On the basis of the results the prioritization of Building Information Modeling (BIM) capabilities in the BIM economic capabilities, based on the preferences of c3, c2, c4, and c1.

Table 5
The average matrix of pairwise comparisons of subcriteria related to criteria F. The matrix of pairwise comparisons main criteria.Model main criteria and subcriteria relative weight.
Note: The hierarchy of human resources management capabilities, arranged in order of preference, is as follows: e1, e3, e2, and e4.T A B L E 7