Construction projects frequently face risks such as anomalies, delays, and bottlenecks, which can substantially affect timelines and budgets. This study proposes a machine learning (ML)-based framework for early identification of risks in construction projects, enabling pattern understanding and decision-making through clustering, outlier and bottleneck detection, and relevant variables identification. It uses a business process management (BPM) dataset of construction documents and applies clustering techniques to both numerical and mixed datasets to group documents with similar characteristics, enabling the detection of temporal deviations and the patterns behind them. Additionally, an ensemble anomaly detection model based on different algorithms is implemented to identify outliers through key variables, which may indicate hidden risks and planning errors. Explainable artificial intelligence (XAI) techniques are then used to analyse the importance of the variables, supporting the identification and analysis of bottlenecks that may compromise project success. The results reveal an F1 score of 0.73 in bottleneck detection using three understandable decision rules, a 6% rate of anomalies within the dataset, and three distinct project clusters. This approach enables accurate and timely detection of risks while providing valuable insights for decision-making, improving risk management, and optimising project execution in the architecture, engineering and construction (AEC) industry.
Zorrilla et al. (Thu,) studied this question.