State University of Trade and Economics, Department of Management, Kyiv, Ukraine
Abstract
High uncertainty, delays, and budget overruns that characterize the construction industry restrict the usefulness of the conventional expert-based risk management. The growing access to digital project information opens possibilities of data-oriented and predictive solutions, even though current studies are still divided and not grounded in real-life validation. The proposed study will implement and test an integrated software-based digital risk management system of construction projects, which integrates BIM and project management data with machine learning algorithms (Random Forest, LSTM, and XGBoost). The study uses a quantitative design that is based on data from 15 finished residential and commercial construction projects, as well as scenarios that were made up. The results show better performance, with about 82% accuracy in risk classification and minimizing cost and schedule prediction errors by up to 55%. The higher the accuracy of the forecasts is, the more complete and regularly updated the project data is. In general, the results support the idea that integrated information-based systems are more effective in forecasting and decision-making; however, their success is determined by the quality of data, the willingness of organizations, and the presence of experts in managing rare events.
Keywords
Big data,Construction projects,Digital tools,Machine learning,Risk management
How to Cite
BAI, S., LOSITSKA, T., & BUKHANENKO , I. (2026). An Integrated Digital Risk Management System for Construction Projects Using Machine Learning. MAS Journal of Applied Sciences, 11(3), 524–539. https://doi.org/10.5281/zenodo.21808953
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Tatiana LOSITSKA