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Mrs. L. Sunanda is an Assistant Professor in the Department of Information Technology at NRCM, Telangana, India. Her research interests include Data Analytics, Artificial Intelligence, Information Systems, and Smart Computing Technologies.
Construction projects are naturally susceptible to numerous hazards, including budget overruns, timeline delays, safety events, resource deficiencies, environmental uncertainty, and quality-related concerns. Conventional risk assessment techniques frequently depend on expert opinion and historical evaluation, which may prove inadequate for addressing the intricacies of contemporary construction projects. Machine Learning (ML) provides sophisticated predictive abilities by examining extensive project data to uncover concealed patterns, anticipate potential risks, and facilitate proactive decision-making. Utilising techniques like Decision Trees, Random Forests, Support Vector Machines, Artificial Neural Networks, and Deep Learning models, construction stakeholders can enhance project planning, risk management, and operational efficiency. This chapter examines the ideas, methodology, applications, and challenges of machine learning-based construction risk prediction systems, emphasising their contribution to project success and the digital transformation of the construction sector.