Data-Driven Success in Infrastructure Megaprojects. : Leveraging Machine Learning and Expert Insights for Enhanced Prediction and Efficiency

Abstract: This Master's thesis utilizes random forest and leave-one-out cross-validation to predict the success of megaprojects involving infrastructure. The goal was to enhance the efficiency of the design and engineering phase of the infrastructure and construction industries. Due to the small sample size of megaprojects and limitated data sharing, the lack of data poses significant challenges for implementing artificial intelligence for the evaluation and prediction of megaprojects. This thesis explore how megaprojects can benefit from data collection and machine learning despite small sample sizes. The focus of the research was on analyzing data from thirteen megaprojects and identifying the most influential data for machine learning analysis. The results prove that the incorporation of expert data, representing critical success factors for megaprojects, significantly enhanced the accuracy of the predictive model. The superior performance of expert data over economic data, experience data, and documentation data demonstrates the significance of domain expertise. In addition, the results demonstrate the significance of the planning phase by implementing feature selection techniques and feature importance scores. In the planning phase, a small, devoted, and highly experienced team of project planners has proven to be a crucial factor for project success. The thesis concludes that in order for companies to maximize the utility of machine learning, they must identify their critical success factors and collect the corresponding data.