Digitalizing Early Project Delivery: Machine Learning–Enabled Urban Regulatory Intelligence for Governance and Feasibility Decisions

Some of the most complex challenges in construction projects arise during the early stages of project delivery, where regulatory uncertainty and limited visibility of urban conditions often constrain feasibility assessments and delay coordination among project stakeholders. In this context, this study proposes a machine learning–based approach that enables the exploration, clustering, and classification of urban areas using structured and georeferenced data from construction permits issued by government agencies. A midsized Latin American city is used as a case study, drawing on official permit records collected over a 15-year period. Unsupervised learning techniques ( k -means) were applied to identify latent urban development patterns, followed by supervised models [random forest and extreme gradient boosting (XGBoost)] to classify construction zones based on functional and morphological features. The findings demonstrate how automated urban analysis generates consistent and interpretable indicators related to building density, location, and construction typology, which are directly relevant to early project delivery decisions. Beyond predictive performance, the study demonstrates that machine learning–powered urban regulatory intelligence can aid early project governance by decreasing information asymmetry, providing regulatory insights upfront, and fostering earlier alignment among project owners, designers, and contractors. These features are essential for collaborative delivery methods like integrated project delivery and lean project delivery, where early coordination, transparent information sharing, and a common understanding of constraints are crucial for ensuring project success.