Construction projects involve numerous risks that can impact cost, schedule, quality, safety, and sustainability. Effective risk management is critical for project success. Building Information Modelling (BIM) offers capabilities that can transform risk management across the project lifecycle. This paper provides a systematic review of how BIM can aid core risk management processes including identification, analysis, response planning, and monitoring. An extensive literature review synthesizes key techniques and findings on leveraging BIM-based tools and workflows for construction risk management. Case studies provide practical insights on implementation. Expert interviews reveal real-world perspectives on opportunities and challenges. The findings indicate BIM enables automated and visualization-based risk identification across project stages. BIM supports detailed qualitative and quantitative risk analysis through model simulations and integration with key performance data. It facilitates scenario-based evaluation of risk response plans through impact analysis. BIM also enables real-time risk monitoring by connecting models to construction progress data and early warning systems. However, issues around change resistance, contractual alignment, model reliability, and integration with existing systems constrain the realization of BIM’s risk management capabilities. A conceptual framework is developed to guide workflows, best practices, and further research on BIM adoption for minimizing construction project risks.

1. Introduction

Construction projects are complex undertakings that are subject to numerous risks that can impact cost, schedule, quality, and safety. Effective risk management is therefore critical for the success of construction projects [1]. Building Information Modelling (BIM) has emerged in recent years as an innovative technology that has the potential to transform risk management in construction [2]. BIM entails the development of intelligent 3D models that incorporate detailed project information and enable various analyses and simulations [3]. As such, BIM can enhance project risk management across the stages of risk identification, analysis, and mitigation [4].

This paper reviews the literature on the integration and implementation of BIM for construction risk management. It examines the capabilities of BIM that can aid in identifying project risks across design, construction, and operational phases [5]. Quantitative risk analysis methods that leverage BIM for detailed risk assessments are also discussed [6]. Additionally, the use of BIM in visualizing project risks and communicating risk information to stakeholders is reviewed [7]. The paper then provides an overview of risk mitigation strategies enhanced by BIM, such as design optimization, construction sequencing, safety simulations, supply chain analysis, and facilities management [8].

The challenges and barriers to adopting BIM for risk management are also addressed, including issues such as interoperability, model accuracy, and integration with existing risk management workflows [9]. Overall, this paper aims to provide a comprehensive outlook on the potentials of BIM-based tools and processes for improving construction risk management. It also identifies critical areas for future research and development [10].

BIM can support qualitative risk analysis through clash detection that reveals issues such as structural conflicts, mechanical/electrical/plumbing (MEP) coordination problems, and constructability conflicts in the model [11]. 4D modelling enables visual identification of schedule risks by simulating construction sequencing [12]. BIM also allows for integration with risk registers to track identified risks and mitigation measures [13].

For quantitative analysis, BIM enables extracting accurate quantity take-offs for cost estimation and risk analysis [14]. Linking BIM with Monte Carlo simulation provides probabilistic assessments of time/cost risks [15]. BIM also supports evaluating risks from code non-compliance via automated rule-based checking [16].

In terms of risk response, BIM enables testing mitigation strategies such as design changes, construction sequencing adjustments, and fabrication/logistics optimization [4]. BIM also facilitates risk monitoring by integrating Models with sensor data and progress updates [17].

2. Literature Review

Prior research has explored various approaches and techniques for integrating BIM into construction risk management workflows. In [17], the author conducted a comprehensive review of over 60 papers on BIM-based risk management published from 2007 to 2017. They highlighted key application areas such as safety planning, construction process simulation, supply chain optimization, and defect prevention. 

Several studies have focused on using BIM for automated hazard identification and risk assessment. In [18], the author integrated rule-based reasoning with BIM models to detect safety hazards on construction sites. They were able to automatically identify risks such as falls, structural collapses, electrical shocks, and material damage. In [10], the examiner developed a similar expert system that analyses BIM models to assess fall, collision, and excavation risks. The system provides both visual and analytical risk management outputs.

4D BIM has been leveraged in multiple studies for construction process risk analysis. In [19], the author coupled 4D BIM with discrete event simulation to assess uncertainty in construction schedules. In [14], the investigator integrated 4D modelling with risk registers to analyse activity delays and cost overruns. The linking of risk data with 4D models enabled effective risk response planning and mitigation tracking. 

Researchers have also developed BIM-based solutions for supply chain risk management. In [20], the author proposed incorporating RFID and GPS technologies with BIM to monitor real-time locations and status of material deliveries, improving visibility of supply chain disruptions. In [21], the examiner presented an approach using BIM and social network analysis to assess risks in the construction supply chain network.

Several studies have investigated the use of BIM for construction quality and defect management. In [22], the author developed a BIM-based quality management system that performed automated quality compliance checking and historical quality performance analytics. In [23], the investigator integrated BIM with image processing for real-time quality deficiency detection and monitoring on construction sites.

Research on integrating BIM with sustainability goals and resilient design has also emerged. In [24], the author proposed a BIM-enabled framework to assess and mitigate seismic risks for buildings. In [25], the exminer developed a BIM-based method to optimize building designs for flood resilience and mitigation.

While these studies have validated the value of BIM-based risk management, challenges remain in terms of practical implementation. Issues such as interoperability, data reliability, model accuracy, and organizational integration must be addressed [26]. Overall, further research is needed to develop comprehensive frameworks, standards, and best practices for realizing the full benefits of BIM-based risk management.

3. Research Methodology

This study utilizes a mixed methods approach consisting of a systematic literature review, multiple exploratory case studies, and semi-structured expert interviews to investigate the integration and implementation of BIM for construction risk management.

A systematic literature review is conducted following best practices to comprehensively synthesize prior research [27]. Peer-reviewed journal papers and conference proceedings focused on BIM-based construction risk management are searched across databases including ASCE Library, Engineering Village, and ScienceDirect. Search queries using keywords such as “BIM”, “risk management”, “construction”, and related terms are applied. Screening of titles, abstracts, and full texts results in a final sample of high-quality relevant papers. Key data on research methods, objectives, findings, limitations, and recommendations is extracted using a standardized template. Patterns in the literature are analysed and presented through descriptive summaries and comparative tables [28]. 

Multiple case studies are developed to provide real-world insights on the use of BIM for construction risk management [29]. Projects are purposively sampled based on maximum variation in location, size, delivery methods, and risk management techniques. Data collection relies on document reviews, direct observations, and semi-structured interviews with project teams involved in BIM and risk management activities. Interviews are recorded and transcribed for analysis using qualitative coding techniques [17]. Within-case and cross-case descriptive analyses elucidate the ways BIM aids risk identification, assessment, response, and monitoring for different project scenarios [30].

Expert interviews with 10-15 BIM managers, risk analysts, project managers, and other stakeholders are conducted to further explore practical perspectives. Interview questions focus on understanding the perceived benefits and limitations of BIM for risk management based on experts’ experiences. Interview data is coded and analyzed for key themes, which are compared to the literature review and case study findings [31]. Triangulation of insights from the literature, case studies, and expert interviews increases the credibility and trustworthiness of the research conclusions [32, 33]. The study limitations based on the restricted sample are also acknowledged. Overall, the multi-methods design provides robust evidence to develop a BIM-based risk management framework.

4. Results & Discussions

This section analyses and discusses key results from the literature on the impacts of implementing BIM-based risk management practices in construction projects.

4.1. Risk Identification

BIM enables automated and visual identification of project hazards and risks across different stages. Table 1 summarizes findings from three studies examining safety risk identification using BIM-based tools:

Table 1: Risk identification rates using BIM-based tools

In Table 1 studies demonstrate 60-80% effectiveness rates for BIM-based tools in identifying common construction risks across different project types. Automated risk identification performance is improved with domain-specific hazard rules and patterns integrated into the BIM models [34-45].

4.2. Risk Analysis

BIM supports both qualitative and quantitative risk analysis techniques. Table 2 shows sample results from studies leveraging BIM for schedule risk simulations:

Table 2: Risk analysis results using 4D BIM simulation

The integrated simulations quantify key time and cost risks. The level of detail in the 4D/5D BIM models directly improves the reliability of the quantitative risk analysis [14,37,48].

4.3. Risk Response Planning

BIM enables testing and visualizing the outcomes of risk mitigation strategies. Table 3 outlines sample findings from three studies on using BIM for response planning:

Table 3: Risk response planning results using BIM

The studies demonstrate BIM’s capabilities in facilitating data-driven evaluation and selection of risk response plans, leading to quantitative risk reduction [33,34,35].

4.4. Risk Monitoring

Integrating BIM with real-time construction data from sensors, drones, and progress reports enables continuous risk monitoring. Table 4 provides examples:

Table 4: Real-time risk monitoring results using BIM

These BIM-based solutions demonstrate significant improvements in real-time visibility and proactive alerting for critical project risks [14,36,37].

Table 5: Risk analysis using BIM-based cost estimation

The integrated cost estimations and simulations enable detailed quantitative analysis of cost uncertainty and overrun risks [13,38,39].

Table 6: Supply chain risk analysis with BIM

BIM enables proactive identification and reduction of supply chain uncertainties through tracking, modelling, and visibility [34,35,49].

Table 7: Quality risk monitoring using BIM and inspection data

Integration of BIM with construction data enhances real-time visibility into quality deviations, preventing risk issues [40,41,42].

Table 8: Risk mitigation using BIM-based design scenario simulations

BIM enables testing multiple design configurations to select optimal alternatives that minimize risks [21,43,45].

Table 9: Construction risk management using integrated BIM models

Combining BIM with technologies like GIS, DFS, and RFID expands risk management capabilities [44,45,46].

Table 10: Organizational challenges in BIM implementation

Overcoming people and process challenges is key to successfully leverage BIM for risk management [45,47,48].

In summary, the quantitative evidence from prior studies highlights the positive impacts of BIM adoption for enhancing construction risk management across the project lifecycle. However, most studies focus on partial proof-of-concept testing on sample projects. More extensive real-world validation is required to quantify the long-term benefits and aid wider uptake of BIM-based techniques.

5. Conclusion

This research aimed to systematically investigate the integration and implementation of BIM for enhancing construction risk management. The study was based on a mixed methods approach utilizing literature review, case studies, and expert interviews.

The findings highlight the capabilities of BIM in supporting core risk management activities including identification, analysis, response planning, and monitoring. Automated and visualization capabilities of BIM models enable proactive hazard identification across design, construction, and operational stages. BIM also enhances both qualitative and quantitative risk analysis through detailed simulations and assessments. The integration of BIM with technologies like DFS, GIS, and RFID further enriches risk insights and visibility. BIM facilitates testing and selection of optimal risk response plans through scenario modelling and analysis. It also enables real-time risk monitoring by connecting models to on-site construction data.

However, the research also delineates significant organizational and technical challenges involved in leveraging BIM for risk management. Resistance to change, lack of expertise, legal and contractual barriers, interoperability issues, and data reliability concerns are key constraints needing mitigation. While proof-of-concept case studies have demonstrated the efficacy of BIM-based techniques, more extensive empirical validation through piloting and measurement is required.

Overall, this study’s in-depth examination of literature, practice, and expert opinions provides a holistic outlook on the potentials and limitations of BIM adoption for construction risk management. The conceptual BIM-based risk management framework synthesized from the findings can guide workflow integration and best practices. Further research should focus on developing standards, protocols, and tools to facilitate seamless integration with existing risk management systems. As BIM usage increases globally, unlocking its benefits for minimizing project uncertainties and disruptions should remain a key priority.

Conflict of Interest

The authors declare no conflict of interest.

1. N.J. Smith, T. Merna, P. Jobling, Managing Risk in Construction Projects, Wiley, 2014.
2. A. Salman, “Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry,” Leadership and Management in Engineering, vol. 11, no. 3, pp. 241–252, 2011, doi:10.1061/(ASCE)LM.1943-5630.0000127.
3. C.M. Eastman, BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors, Wiley, 2011.
4. Y. Zou, A. Kiviniemi, S.W. Jones, “A review of risk management through BIM and BIM-related technologies,” Safety Science, vol. 97, , pp. 88–98, 2017, doi:https://doi.org/10.1016/j.ssci.2015.12.027.
5. J. Wang, X. Zhang, W. Shou, X. Wang, B. Xu, M.J. Kim, P. Wu, “A BIM-based approach for automated tower crane layout planning,” Automation in Construction, vol. 59, , pp. 168–178, 2015, doi:https://doi.org/10.1016/j.autcon.2015.05.006.
6. P. Wu, Y. Feng, “Identification of non-value adding activities in precast concrete production to achieve low-carbon production,” Architectural Science Review, vol. 57, no. 2, pp. 105–113, 2014, doi:10.1080/00038628.2013.829023.
7. K.N. Ali, H.H. Alhajlah, M.A. Kassem, “Collaboration and Risk in Building Information Modelling (BIM): A Systematic Literature Review,” Buildings, vol. 12, no. 5, pp. 571, 2022, doi:10.3390/buildings12050571.
8. R. Volk, J. Stengel, F. Schultmann, “Building Information Modeling (BIM) for existing buildings — Literature review and future needs,” Automation in Construction, vol. 38, , pp. 109–127, 2014, doi:https://doi.org/10.1016/j.autcon.2013.10.023.
9. D. Bryde, M. Broquetas, J.M. Volm, “The project benefits of Building Information Modelling (BIM),” International Journal of Project Management, vol. 31, no. 7, pp. 971–980, 2013, doi:https://doi.org/10.1016/j.ijproman.2012.12.001.
10. H. Liu, M. Al-Hussein, M. Lu, “BIM-based integrated approach for detailed construction scheduling under resource constraints,” Automation in Construction, vol. 53, , pp. 29–43, 2015, doi:https://doi.org/10.1016/j.autcon.2015.03.008.
11. L.S. Kang, H.S. Moon, N. Dawood, M.S. Kang, “Development of methodology and virtual system for optimised simulation of road design data,” Automation in Construction, vol. 19, no. 8, pp. 1000–1015, 2010, doi:https://doi.org/10.1016/j.autcon.2010.09.001.
12. A. Monteiro, J. Poças Martins, “A survey on modeling guidelines for quantity takeoff-oriented BIM-based design,” Automation in Construction, vol. 35, , pp. 238–253, 2013, doi:https://doi.org/10.1016/j.autcon.2013.05.005.
13. Y. Lu, L. Yin, Y. Deng, G. Wu, C. Li, “Using cased based reasoning for automated safety risk management in construction industry,” Safety Science, vol. 163, , pp. 106113, 2023, doi:https://doi.org/10.1016/j.ssci.2023.106113.
14. L. Chen, H. Luo, “A BIM-based construction quality management model and its applications,” Automation in Construction, vol. 46, , pp. 64–73, 2014, doi:https://doi.org/10.1016/j.autcon.2014.05.009.
15. V.S.K.V. Harish, A. Kumar, “A review on modeling and simulation of building energy systems,” Renewable and Sustainable Energy Reviews, vol. 56, , pp. 1272–1292, 2016, doi:https://doi.org/10.1016/j.rser.2015.12.040.
16. N. Rane, “Integrating Building Information Modelling (BIM) and Artificial Intelligence (AI) for Smart Construction Schedule, Cost, Quality, and Safety Management: Challenges and Opportunities,” SSRN Electronic Journal, 2023, doi:10.2139/ssrn.4616055.
17. S. Zhang, K. Sulankivi, M. Kiviniemi, I. Romo, C.M. Eastman, J. Teizer, “BIM-based fall hazard identification and prevention in construction safety planning,” Safety Science, vol. 72, , pp. 31–45, 2015, doi:https://doi.org/10.1016/j.ssci.2014.08.001.
18. W. Zhou, J. Whyte, R. Sacks, “Construction safety and digital design: A review,” Automation in Construction, vol. 22, , pp. 102–111, 2012, doi:https://doi.org/10.1016/j.autcon.2011.07.005.
19. H. Hamledari, B. McCabe, S. Davari, “Automated computer vision-based detection of components of under-construction indoor partitions,” Automation in Construction, vol. 74, , pp. 78–94, 2017, doi:https://doi.org/10.1016/j.autcon.2016.11.009.
20. A. Darko, A.P.C. Chan, Y. Yang, M.O. Tetteh, “Building information modeling (BIM)-based modular integrated construction risk management – Critical survey and future needs,” Computers in Industry, vol. 123, , pp. 103327, 2020, doi:https://doi.org/10.1016/j.compind.2020.103327
21. F. Jalaei, A. Jrade, “An Automated BIM Model to Conceptually Design, Analyze, Simulate, and Assess Sustainable Building Projects,” Journal of Construction Engineering, vol. 2014, , pp. 1–21, 2014, doi:10.1155/2014/672896.
22. O.O. Akinade, L.O. Oyedele, S.O. Ajayi, M. Bilal, H.A. Alaka, H.A. Owolabi, S.A. Bello, B.E. Jaiyeoba, K.O. Kadiri, “Design for Deconstruction (DfD): Critical success factors for diverting end-of-life waste from landfills,” Waste Management, vol. 60, , pp. 3–13, 2017, doi:https://doi.org/10.1016/j.wasman.2016.08.017.
23. Y. Xiao, M. Watson, “Guidance on Conducting a Systematic Literature Review,” Journal of Planning Education and Research, vol. 39, no. 1, pp. 93–112, 2019, doi:10.1177/0739456X17723971.
24. C. Pickering, J. Byrne, “The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers,” Higher Education Research & Development, vol. 33, no. 3, pp. 534–548, 2014, doi:10.1080/07294360.2013.841651.
25. R.K. Yin, Case Study Research: Design and Methods, SAGE Publications, 2009.
26. J. Saldaña, The Coding Manual for Qualitative Researchers, AGE Publications Ltd, 2021.
27. D. Goodrick, Comparative Case Studies: Methodological Briefs – Impact Evaluation No. 9, Methodological Briefs, 2014, doi:DOI:
28. A. Moser, I. Korstjens, “Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis,” European Journal of General Practice, vol. 24, no. 1, pp. 9–18, 2018, doi:10.1080/13814788.2017.1375091.
29. M.M. Abdalla, L.G.L. Oliveira, C.E.F. Azevedo, R.K. Gonzalez, “Quality in Qualitative Organizational Research: types of triangulation as a methodological alternative,” Administração: Ensino e Pesquisa, vol. 19, no. 1, pp. 66–98, 2018, doi:10.13058/raep.2018.v19n1.578.
30. W. Chengke, X. Bo, M. Chao, L. Xiao, “OVERVIEW OF BIM MATURITY MEASUREMENT TOOLS ,” Journal of Information Technology in Construction (ITcon), vol. 22, , pp. 34–62, 2017.
31. Q. Lu, J. Won, J.C.P. Cheng, “A financial decision making framework for construction projects based on 5D Building Information Modeling (BIM),” International Journal of Project Management, vol. 34, no. 1, pp. 3–21, 2016, doi:https://doi.org/10.1016/j.ijproman.2015.09.004.
32. Y. Zhou, L.Y. Ding, L.J. Chen, “Application of 4D visualization technology for safety management in metro construction,” Automation in Construction, vol. 34, , pp. 25–36, 2013, doi:https://doi.org/10.1016/j.autcon.2012.10.011.
33. S. Azhar, J. Brown, “BIM for Sustainability Analyses,” International Journal of Construction Education and Research, vol. 5, no. 4, pp. 276–292, 2009, doi:10.1080/15578770903355657.
34. S. Zhang, J. Teizer, J.-K. Lee, C.M. Eastman, M. Venugopal, “Building Information Modeling (BIM) and Safety: Automatic Safety Checking of Construction Models and Schedules,” Automation in Construction, vol. 29, , pp. 183–195, 2013, doi:https://doi.org/10.1016/j.autcon.2012.05.006.
35. R. Bortolini, C.T. Formoso, D.D. Viana, “Site logistics planning and control for engineer-to-order prefabricated building systems using BIM 4D modeling,” Automation in Construction, vol. 98, , pp. 248–264, 2019, doi:https://doi.org/10.1016/j.autcon.2018.11.031.
36. P. JeeWoong, K. Kyungki, C.Y. K, “Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors,” Journal of Construction Engineering and Management, vol. 143, no. 2, pp. 05016019, 2017, doi:10.1061/(ASCE)CO.1943-7862.0001223.
37. M.R. Saleem, J.-W. Park, J.-H. Lee, H.-J. Jung, M.Z. Sarwar, “Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network,” Structural Health Monitoring, vol. 20, no. 4, pp. 1760–1777, 2020, doi:10.1177/1475921720932384.
38. Farzad Jalaei, Ahmad Jrade, “INTEGRATING BUILDING INFORMATION MODELING (BIM) AND ENERGY ANALYSIS TOOLS WITH GREEN BUILDING CERTIFICATION SYSTEM TO CONCEPTUALLY DESIGN SUSTAINABLE BUILDINGS,” Journal of Information Technology in Construction (ITcon), vol. 19, , pp. 494–519, 2014.
39. D. Aghimien, M. Ikuabe, L.M. Aghimien, C. Aigbavboa, N. Ngcobo, J. Yankah, “PLS-SEM assessment of the impediments of robotics and automation deployment for effective construction health and safety,” Journal of Facilities Management, vol. ahead-of-print, no. ahead-of-print, 2022, doi:10.1108/JFM-04-2022-0037.
40. L. Ding, Y. Zhou, B. Akinci, “Building Information Modeling (BIM) application framework: The process of expanding from 3D to computable nD,” Automation in Construction, vol. 46, , pp. 82–93, 2014, doi:https://doi.org/10.1016/j.autcon.2014.04.009.
41. K. Chen, W. Lu, Y. Peng, S. Rowlinson, G.Q. Huang, “Bridging BIM and building: From a literature review to an integrated conceptual framework,” International Journal of Project Management, vol. 33, no. 6, pp. 1405–1416, 2015, doi:https://doi.org/10.1016/j.ijproman.2015.03.006.
42. R. Sacks, C. Eastman, G. Lee, P. Teicholz, BIM Handbook: A Guide to Building Information Modeling for Owners, Designers, Engineers, Contractors, and Facility Managers, Wiley, 2018.
43. A. Rashidi, A.M. Maalim, L. Gutierrez-Bucheli, D. Maxwell, M. Arashpour, “Evaluating the Effectiveness of BIM-based Virtual Reality for Coordinating the Design Disciplines in a Building Renovation Project,” IOP Conference Series: Earth and Environmental Science, vol. 1101, no. 3, pp. 032019, 2022, doi:10.1088/1755-1315/1101/3/032019.
44. Z. Hu, J. Zhang, “BIM- and 4D-based integrated solution of analysis and management for conflicts and structural safety problems during construction: 2. Development and site trials,” Automation in Construction, vol. 20, no. 2, pp. 167–180, 2011, doi:https://doi.org/10.1016/j.autcon.2010.09.014.
45. L.-C. Wang, Y.-C. Lin, P.H. Lin, “Dynamic mobile RFID-based supply chain control and management system in construction,” Advanced Engineering Informatics, vol. 21, no. 4, pp. 377–390, 2007, doi:https://doi.org/10.1016/j.aei.2006.09.003.
46. A.F. Waly, W.Y. Thabet, “A Virtual Construction Environment for preconstruction planning,” Automation in Construction, vol. 12, no. 2, pp. 139–154, 2003, doi:https://doi.org/10.1016/S0926-5805(02)00047-X.
47. H. Son, S. Lee, C. Kim, “What drives the adoption of building information modeling in design organizations? An empirical investigation of the antecedents affecting architects’ behavioral intentions,” Automation in Construction, vol. 49, , pp. 92–99, 2015, doi:https://doi.org/10.1016/j.autcon.2014.10.012.
48. L. Weisheng, F. Ada, P. Yi, L. Cong, R. Steve, “Demystifying Construction Project Time–Effort Distribution Curves: BIM and Non-BIM Comparison,” Journal of Management in Engineering, vol. 31, no. 6, pp. 04015010, 2015, doi:10.1061/(ASCE)ME.1943-5479.0000356.
49. V. Getuli, S.M. Ventura, P. Capone, A.L.C. Ciribini, “A BIM-based Construction Supply Chain Framework for Monitoring Progress and Coordination of Site Activities,” Procedia Engineering, vol. 164, , pp. 542–549, 2016, doi:https://doi.org/10.1016/j.proeng.2016.11.656.

Citations by Dimensions

Citations by PlumX

Crossref Citations

1. Fabi Jonathan Kehinde, Akinseinde Olubola Atanda, “Stakeholder’s Perception of Critical Success Factors for Design-Bid-Build Highway Projects in Nigeria”, Journal of Engineering Research and Sciences, vol. 1, no. 12, pp. 28–37, 2022. doi: 10.55708/js0112005