Automation of Construction Risk Management Processes Based on Machine Learning Algorithms: Problem Formulation and Efficiency Analysis
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Razgonau Aliaksandr
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The article examines theoretical and applied aspects of automating construction risk management processes using machine learning algorithms. It is shown that under conditions of high uncertainty in construction projects, traditional risk management approaches are limited in terms of responsiveness and forecasting accuracy. A conceptual model of risk management automation is considered, taking into account schedule, cost, quality, resource, and contractual factors. The main classes of machine learning algorithms applicable to risk prediction and ranking are analyzed. Based on a comparative analysis of published practical cases from large construction companies, the applied effects of implementing algorithmic solutions are demonstrated, including reduced losses, increased transparency of control, and improved quality of managerial decision-making.
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Razgonau Aliaksandr
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References:
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