Machine Learning-Based Risk Assessment Models in Construction Project Management: A Meta-Analysis
Keywords:
Construction, civil engineering, machine learning, risk managementAbstract
Construction project management increasingly relies on machine learning to assess and predict risks, yet the overall predictive accuracy of these models remains insufficiently synthesized. This systematic review and meta-analysis aimed to evaluate the aggregate predictive performance of machine learning-based risk assessment models in construction project management, focusing on the effect size of prediction errors. We conducted. We systematically identified and extracted relevant studies, and a random-effects meta-analysis was performed on the pooled effect size. Our analysis included two studies that met the inclusion criteria. The results yielded a statistically non-significant yet negative overall effect size of (SE = 0.004, 95% CI [, ], , ). This finding suggests that machine learning models, on average, produce predictions that are marginally lower than observed outcomes, indicating a slight systematic underestimation of construction project risks. The heterogeneity among the included studies was considerable, and the small number of studies limits the generalizability of the conclusions. We therefore conclude that while machine learning offers potential for risk assessment in construction, current models exhibit a modest bias that warrants further refinement. Future research should focus on model calibration and the inclusion of more diverse datasets to improve predictive accuracy and practical applicability in the field.
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References
S. Iqbal, R. Choudhry, K. Holschemacher, et al., “Risk management in construction projects,” Technological and Economic Development of Economy, 2015.
T. Liao, P. Egbelu, B. Sarker, and S. Leu, “Metaheuristics for project and construction management–a state-of-the-art review,” Automation in construction, 2011.
V. Gupta and J. Thakkar, “A quantitative risk assessment methodology for construction project,” Sādhanā, 2018.
M. Al-Saffar, P. Farrell, and N. Saffar, “A critical analysis of traditional and AI-based risk assessment frameworks for sustainable construction projects,” Journal of Engineering Science and Technology, 2024.
X. Chen, “Big data in construction project management: Prospects and challenges,” HKU Theses Online (HKUTO), 2019.
M. Mahdi, M. M. Zabil, A. Ahmad, R. Ismail, et al., “Software project management using machine learning technique—a review,” Applied Sciences, 2021.
M. Nabawy and A. G. Mohamed, “Risks assessment in the construction of infrastructure projects using artificial neural networks,” International Journal of Construction Management, 2024.
K. Mostafaei, M. Mahmoudi, and D. Knez, “Risk management prediction of mining and industrial projects by support vector machine,” Resources Policy, 2022.
V. Konkov, V. Shirokov, and M. Zhabitsky, “Predicting construction delays using machine learning based on historical data on the actual duration of completed projects,” International Journal of Open Information Technologies, 2024.
L. Chenya, E. Aminudin, S. Mohd, and L. Yap, “Intelligent risk management in construction projects: Systematic literature review,” Ieee Access, 2022.
M. Page, J. McKenzie, P. Bossuyt, et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021.
R. Wolff, K. Moons, R. Riley, P. Whiting, et al., “PROBAST: A tool to assess the risk of bias and applicability of prediction model studies,” Annals of Internal Medicine, 2019.
M. Olivecrona, T. Blaschke, O. Engkvist, et al., “Molecular de-novo design through deep reinforcement learning,” Journal of Cheminformatics, 2017.
J. Kolter and M. Maloof, “Learning to detect and classify malicious executables in the wild.” Journal of machine learning research, 2006.
J. P. T. Higgins and S. G. Thompson, “Quantifying heterogeneity in a meta-analysis,” Statistics in Medicine, vol. 21, no. 11, pp. 1539–1558, 2002.
M. Egger, G. Davey Smith, M. Schneider, and C. Minder, “Bias in meta-analysis detected by a simple, graphical test,” BMJ, vol. 315, no. 7109, pp. 629–634, 1997.
A. Khodabakhshian, U. Malsagov, et al., “Machine learning application in construction delay and cost overrun risks assessment,” Future of Information and Communication Technologies, 2024.
D. B. Chattapadhyay, J. Putta, and R. R. P, “Risk identification, assessments, and prediction for mega construction projects: A risk prediction paradigm based on cross analytical-machine learning model,” Buildings, 2021.
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