Risk Analysis In Construction Projects Using Machine Learning Methods

Risk analysis is a critical component of successful construction project management, particularly given the complexity, uncertainty, and likelihood of delays and cost overruns inherent in such projects. This paper examines the application of machine learning techniques to enhance the identification, assessment, and mitigation of risks in construction projects. Using real-world project data, this study employs supervised and unsupervised learning algorithms to predict potential risks and their impacts. Key findings reveal that machine learning models, such as decision trees, neural networks, and support vector machines, outperform traditional risk management methods in terms of accuracy and efficiency. Furthermore, the paper highlights the integration of machine learning tools into existing project management systems, enabling real-time risk monitoring and data-driven decision-making. The results indicate that leveraging machine learning can significantly improve project outcomes by reducing uncertainties and enhancing proactive risk management strategies.