Buildings 2025, 15(21), 3947; https://doi.org/10.3390/buildings15213947 (registering DOI) - 1 Nov 2025
Abstract
Accurately estimating total accident costs is essential for managing construction safety budgets. While direct costs are well-documented, indirect costs—such as productivity loss, material damage, and legal expenses—are difficult to predict and often overlooked. Traditional ratio-based methods lack accuracy due to variability across projects
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Accurately estimating total accident costs is essential for managing construction safety budgets. While direct costs are well-documented, indirect costs—such as productivity loss, material damage, and legal expenses—are difficult to predict and often overlooked. Traditional ratio-based methods lack accuracy due to variability across projects and accident types. This study introduces a two-tiered machine learning framework for real-time indirect cost estimation. In the first tier, classification models (decision tree, random forest, k-nearest neighbor, and XGBoost) predict total cost categories; in the second, regression models (decision tree, random forest, gradient boosting, and light-gradient boosting machine) estimate indirect costs. Using a dataset of 1036 construction accidents collected over two years, the model achieved accuracies above 87% in classification and an R2 of 0.95 with a training MSE of 0.21 in regression. Compared to conventional statistical and single-step models, it demonstrated superior predictive performance, reducing average deviations to $362.63 and sometimes achieving zero deviation. This framework enables more precise, real-time estimation of hidden costs, promoting better safety investment, reduced financial risk, and adaptive learning through retraining. When integrated with a national accident cost database, it supports ongoing improvement and informed risk management for construction stakeholders.
Full article
(This article belongs to the Special Issue Data-Driven Project Planning and Control: Advancing Cost, Schedule, and Risk Management)
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