Machine Learning Application in Construction Delay and Cost Overrun Risks Assessment

Construction projects are prone to experience significant delays and cost overruns due to uncontrollable risks raised by their complex, unique, and uncertain nature. Conventional Risk Management methods have proven inefficient, time-consuming, and highly subjective, making exploring innovative and data-driven solutions essential. Artificial Intelligence (AI) is revolutionizing the construction industry by offering improved, optimized, and automatized Project Management solutions, which can benefit existing RM processes significantly. This study investigates the application of various Machine Learning algorithms for delay and cost overrun risk prediction in construction projects. A case study involving NYC school construction projects is used to train and evaluate algorithms such as Decision Trees, Artificial Neural Networks, Extreme Gradient Boosting, and Linear and Ridge regressions. The ultimate goal of this research is to conduct a comparative analysis between the performances and prediction precision of different ML algorithms for delays and cost overruns, two of the most significant construction risks, concerning each algorithm’s structure and learning process. The results of this study provide automated and precise predictions of risks in new construction projects while also contributing valuable insights into the potential and benefits of ML applications in the construction industry.