A Systematic Review of Current Risk Assessment Practices in Construction Projects
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Purpose: This systematic review examines current risk assessment practices in construction projects, analyzing methodological approaches, technological integration, and thematic priorities in literature published between 2020 and 2026. The study aims to identify prevailing risk assessment practices and research gaps while providing a comparative analysis of methodological evolution.
Methodology: Following PRISMA guidelines, a systematic literature review was conducted across Scopus, Web of Science, and Dimensions databases. The search targeted peer-reviewed articles on construction project risk assessment published from January 2020 to February 2026. From 847 initial records, 187 articles met inclusion criteria and were analyzed using bibliometric and content analysis methods with inter-reviewer agreement validation.
Findings: Analysis reveals quantitative methods dominate (47%), followed by qualitative (32%) and mixed-methods (21%). Current practices comprise traditional techniques (31%), advanced methods including AI and machine learning (37%), hybrid approaches (18%), and real-time assessment (14%). Advanced methods demonstrate 15–25% higher prediction accuracy but face implementation barriers including data requirements and interpretability challenges. Real-time assessment, despite 35–50% accident reduction potential in trials, remains limited due to cost and infrastructure constraints. Significantly, 65% of studies exclude post-construction variables, and developing economies remain underrepresented (24%).
Originality: This review provides the first comprehensive synthesis of risk assessment practices spanning 2020-2026, quantifying methodological trends and identifying that 65% of studies lack integration of post-construction variables. The findings establish a baseline for understanding the evolution toward technology-enabled, proactive risk assessment frameworks in construction projects.
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Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial intelligence in construction project management: A structured literature review of its evolution in application and future trends. Digital, 5(3), 26.
Afzal, F., Yunfei, S., Nazir, M., & Bhatti, S. M. (2021). A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies: Cost overrun in construction projects. International Journal of Managing Projects in Business, 14(2), 300-328.
Akhtar, M., Mufti, N. A., Mubin, S., Saleem, M. Q., Zahoor, S., & Ullah, S. (2023). Identification of various execution modes and their respective risks for public–private partnership (PPP) infrastructure projects. Buildings, 13(8), 1889.
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827.
Akinade, Olugbenga, et al. "Design for deconstruction using a circular economy approach: barriers and strategies for improvement." Production planning & control 31.10 (2020): 829-840.
Albasyouni, W., Kamara, J., & Heidrich, O. (2025). Key challenges and opportunities to improve risk assessments in the construction industry. Buildings, 15(11), 1832.
Aljassmi, H., & Han, S. (2013). Analysis of causes of construction defects using fault trees and risk importance measures. Journal of Construction Engineering and Management, 139(7), 870-880.
Almashhour, R., Al-Mhdawi, M. K. S., Daghfous, A., Qazi, A., & Ojiako, U. (2025). Traditional to sustainable risk management in the construction industry: a systematic literature review. International Journal of Managing Projects in Business, 18(3), 528-565.
Almatared, M., Liu, H., Abudayyeh, O., Hakim, O., & Sulaiman, M. (2023). Digital-twin-based fire safety management framework for smart buildings. Buildings, 14(1), 4.
Awolusi, I., Marks, E., & Hallowell, M. (2018). Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Automation in construction, 85, 96-106.
Bao, Y., Pei, C., Mou, Y., Li, M., & Cheng, X. (2025). A convolutional neural network–long short-term memory (CNN–LSTM)–Attention model based on wavelet transform for predicting non-stationary wind pressure coefficients on the surface of terminal glass curtain wall. Science Progress, 108(3), 00368504251366365.
Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., ... & Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced engineering informatics, 30(3), 500-521.
Celik, T., Kamali, M., & Arayici, Y. (2023). Interval type-2 fuzzy AHP for international construction project risk assessment. Expert Systems with Applications, 215, 119342.
Chabalala, N., Weaich, M., Simbanegavi, P., Ndlovu, P., & Gethe, F. (2024). The ripple effects of covid-19 on south africa's building and construction industry: Workforce reduction and project delays. Available at SSRN 4897147.
Chen, C., & Lee, J. (2023). Integrated FMEA-fuzzy TOPSIS approach for construction supply chain risk assessment. International Journal of Construction Management, 23(4), 621-635.
Chen, J., Zhang, R., & Wang, X. (2024). Computer vision-based hazard identification for crane operations on construction sites. Automation in Construction, 158, 105201.
Chen, H. (2024). Enhancing Occupational Safety through Vision-based Integrative Technologies in the Construction and Building Industry (Doctoral dissertation, RMIT University).
Choi, J., Lee, S., & Kim, H. (2023). Hybrid risk assessment system combining case-based reasoning and neural networks for construction delay prediction. Journal of Construction Engineering and Management, 149(5), 04023012.
Darko, A., Chan, A. P. C., Ameyaw, E. E., Owusu, E. K., Pärn, E., & Edwards, D. J. (2019). Review of application of analytic hierarchy process (AHP) in construction. International journal of construction management, 19(5), 436-452.
Dikmen, I., Eken, G., Erol, H., & Birgonul, M. T. (2025). Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning. Computers in Industry, 166, 104251.
El-Sayegh, S. M., Manjikian, S., Ibrahim, A., Abouelyousr, A., & Jabbour, R. (2021). Risk identification and assessment in sustainable construction projects in the UAE. International Journal of Construction Management, 21(4), 327-336.
Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., & Rose, T. (2020). Computer vision-based PPE compliance detection for construction safety. Journal of Building Engineering, 32, 101524.
Fernández-Muñiz, B., Montes-Peón, J. M., & Vázquez-Ordás, C. J. (2024). Risk assessment tools in small and medium construction enterprises. Safety Science, 172, 106401.
Getuli, V., Capone, P., & Bruttini, A. (2024). BIM-based occupational health risk identification during construction. Automation in Construction, 158, 105189.
Han, S., & Lee, S. (2022). Vision-based detection of unsafe ladder use in construction sites. Journal of Computing in Civil Engineering, 36(3), 04022004.
Hassan, M. U., Akram, M., & Ullah, F. (2022). Application of FMEA in high-rise building construction: A case study approach. International Journal of Building Pathology and Adaptation, 40(2), 245-263.
Hwang, S., Park, J., & Lee, H. (2023). Smart helmet with physiological monitoring for construction worker fatigue detection. Sensors, 23(4), 1876.
Jafari Ramiani, A., Sarvari, H., Chan, D. W., Nassereddine, H., & Lotfata, A. (2024). Critical success factors for private sector participation in accomplishing abandoned public sports facilities projects in Iran. International Journal of Construction Management, 24(6), 586-600.
Kabir, S., Papadopoulos, Y., & Agha, M. (2020). Fault tree analysis for construction safety management. Process Safety and Environmental Protection, 142, 211-222.
Kabir, S., Talebi, S., & Papadopoulos, Y. (2022). Bayesian network-based risk assessment for pipeline construction projects. Reliability Engineering & System Safety, 218, 108142.
Kang, H., Sung, S., Hong, J., Jung, S., Hong, T., Park, H. S., & Lee, D. E. (2021). Development of a real-time automated monitoring system for managing the hazardous environmental pollutants at the construction site. Journal of Hazardous Materials, 402, 123483.
Kim, J., Kim, Y., & Choi, J. (2023). Predictive analytics for construction cost performance using historical project data. Journal of Management in Engineering, 39(2), 04022068.
Kim, E. S., & Choi, S. K. (2013). Failure analysis of connecting bolts in collapsed tower crane. Fatigue & Fracture of Engineering Materials & Structures, 36(3), 228-241.
Kochovski, P., & Stankovski, V. (2018). Supporting smart construction with dependable edge computing infrastructures and applications. Automation in Construction, 85, 182-192.
Kumi, L., Jeong, J., & Jeong, J. (2024). Systematic review of quantitative risk quantification methods in construction accidents. Buildings, 14(10), 3306.
Lee, J., Park, Y., & Kim, K. (2024). International construction project risk assessment using machine learning and macroeconomic indicators. Journal of Construction Engineering and Management, 150(2), 04023165.
Li, H., Yang, X., Wang, F., Rose, T., Chan, G., & Dong, S. (2021). Real-time proximity warning system for construction equipment using ultra-wideband technology. Automation in Construction, 132, 103956.
Lin, S. S., Shen, S. L., Zhou, A., & Xu, Y. S. (2021). Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods. Automation in Construction, 122, 103490.
Liu, J., Wang, Y., & Zhang, L. (2023). Fuzzy FMEA with cost-benefit integration for construction risk prioritization. International Journal of Construction Management, 23(8), 1356-1370.
Liu, R., Liu, H., & Zhao, Y. (2021). Fuzzy fault tree analysis for bridge construction risk assessment. Structures, 34, 4521-4533.
Liu, Y., Zhang, P., & Wang, X. (2024). Limitations of traditional risk assessment methods in construction: A critical review. Journal of Civil Engineering and Management, 30(1), 45-62.
Li, Y., & Wang, X. (2018). Risk assessment for public–private partnership projects: using a fuzzy analytic hierarchical process method and expert opinion in China. Journal of Risk Research, 21(8), 952-973.
Medaa, E., Shirzadi Javid, A. A., & Malekitabar, H. (2025). Evolution of Risk Analysis Approaches in Construction Disasters: A Systematic Review of Construction Accidents from 2010 to 2025. Buildings, 15(20), 3701.
Meyer, M., Grisar, C., & Kuhnert, F. (2011). The impact of biases on simulation-based risk aggregation: modeling cognitive influences on risk assessment. Journal of Management Control, 22(1), 79-105.
Moghadam, M., Alvanchi, A., & Alimohammadi, M. (2023). BIM-based constructability risk assessment framework. Journal of Information Technology in Construction, 28, 145-162.
Moheimani, M. A., Sheikhkhoshkar, M., & Banihashemi, S. (2024). Interval type-2 fuzzy TOPSIS for sustainable construction risk assessment. Sustainable Cities and Society, 101, 105078.
Nasirzadeh, F., Khanzadi, M., & Mir, M. (2023). Dynamic modeling of construction project delays using system dynamics and Monte Carlo simulation. Engineering, Construction and Architectural Management, 30(4), 1456-1475.
Nguyen, H. D., Do, Q. N. H., & Macchion, L. (2023). Influence of practitioners' characteristics on risk assessment in Green Building projects in emerging economies: a case of Vietnam. Engineering, construction and architectural management, 30(2), 833-852.
Ojghaz, A. S., Sadeghpour, F., & Bayat, S. (2024). Edge Computing for Real-Time Monitoring Systems in Construction Sites. In Canadian Society of Civil Engineering Annual Conference (pp. 283-292). Cham: Springer Nature Switzerland.
Pereira, F., González García, M. D. L. N., & Poças Martins, J. (2025). Methods and Techniques for Real Time Construction Risk Assessment—Systematic Review. Occupational and Environmental Safety and Health VI: Volume 2: Biomechanics, Occupational Psychosociology and New Trends, 465-480.
Qazi, A., & Simsekler, M. C. E. (2021). Risk assessment of construction projects using Monte Carlo simulation. International journal of managing projects in business, 14(5), 1202-1218.
Qian, W., Min, S., Na, L., Mengqi, S., & Jing, G. (2020). Construction risk assessment of green transformation of old industrial plants based on improved FMEA. In IOP Conference Series: Earth and Environmental Science (Vol. 531, No. 1, p. 012059). IOP Publishing.
Rani, H. A., Syammaun, T., Azzahra, F., Ayob, A., Amlus, M. H., Zakirullah, & Aqsha, M. S. (2024,). Risk management planning by risk register in building construction project. In IOP Conference Series: Earth and Environmental Science (Vol. 1303, No. 1, p. 012034). IOP Publishing.
Resende, B. A. D., Dedini, F. G., Eckert, J. J., Sigahi, T. F., Pinto, J. D. S., & Anholon, R. (2024). Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector. International Journal of Quality & Reliability Management, 41(4), 1063-1088.
Sanni-Anibire, M. O., Mahmoud, A. S., Hassanain, M. A., & Salami, B. A. (2020). A risk assessment approach for enhancing construction safety performance. Safety science, 121, 15-29.
Serrano-Gomez, L., & Munoz-Hernandez, J. I. (2019). Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects. PloS one, 14(6), e0215943.
Shayboun, M., Kifokeris, D., & Koch, C. (2025). A review of machine learning for analysing accident reports in the construction industry. Journal of Information Technology in Construction, 30, 439-460.
Sony, S., Laventure, S., & Sadhu, A. (2021). Vision-based structural health monitoring: A critical review. Engineering Structures, 245, 112891.
Susanto, T., Nurdiana, A., Wibowo, M. A., & Maromi, M. I. (2026). Literature Review of Occupational Safety and Health Risk Management in Foundation Work. Rekayasa Sipil, 20(1), 61-67.
Ullah, S., Deng, X., Amaechi, C. V., Anbar, D. R., & Ashraf, M. W. (2025). A systematic literature review on knowledge mapping for project risk management in the construction industry. Frontiers in Built Environment, 11, 1677904.
Wang, J., Zhang, S., & Liu, J. (2022). Fault tree analysis of tower crane collapses in construction. Engineering Failure Analysis, 138, 106345.
Wang, L., Wang, J., & Zhang, P. (2024). BIM-IoT integration framework for dynamic construction risk assessment. Automation in Construction, 159, 105267.
Wang, Y., Chen, C., & Li, H. (2022). Fuzzy FMEA for high-rise building construction risk assessment. International Journal of Environmental Research and Public Health, 19(8), 4567.
Wuni, I. Y., Shen, G. Q., & Saka, A. B. (2023). Computing the severities of critical onsite assembly risk factors for modular integrated construction projects. Engineering, Construction and Architectural Management, 30(5), 1864-1882.
Wu, S. (2024). Enhancing Construction Workforce Safety through Formulating an Augmented Reality Digital Twin System (Doctoral dissertation, RMIT University).
Yi, W., Chan, A. P. C., & Wang, X. (2022). Environmental sensing network for real-time construction worker exposure monitoring. Journal of Cleaner Production, 376, 134234.
Yussif, A.-M., Taiwo, R., Shakor, P., Han, T., Mohandes, S. R., Antwi Afari, M., Qazi, K., Singh, A. K., Christo, M. S., & Shah, M. A. (2025). A comprehensive literature review on risk identification and assessment in green building construction projects. Cleaner Engineering and Technology, 29, 101089
Zavadskas, E. K., Turskis, Z., & Tamošaitiene, J. (2010). Risk assessment of construction projects. Journal of civil engineering and management, 16(1), 33-46.
Zhang, L., Wu, X., & Skibniewski, M. J. (2021). Bayesian network-based risk assessment for tunnel construction. Tunnelling and Underground Space Technology, 115, 104056.
Zhang, L., Wu, X., & Liu, M. (2021). Fuzzy fault tree analysis for construction safety: A review and future directions. Safety Science, 142, 105387.
Zhang, R., Li, H., & Skitmore, M. (2022). BIM-based rule checking for construction safety risk identification. Automation in Construction, 142, 104512.
Zhong, B., Pan, X., Love, P. E. D., & Ding, L. (2022). Deep learning-based prediction of construction schedule delays. Advanced Engineering Informatics, 54, 101777.
Zou, Y., Kiviniemi, A., & Jones, S. W. (2020). BIM-based risk identification framework for construction projects. Automation in Construction, 118, 103283.
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