A substantial proportion of construction accidents is associated with unsafe worker behavior. Identifying their underlying mechanism is vital for designing effective interventions. As prior studies could not capture complex nonlinear interactions among organizational and individual factors, this study leverages machine learning (ML) techniques, which can capture complex relationships by handling large datasets, and can identify patterns in worker behavior. The study proposes an explainable ML model to interpret key determinants of safe behavior. The data were collected from 425 construction workers in Saudi Arabia. Multiple ensemble and benchmark ML algorithms—including random forest (RF), categorical boosting, decision jungle, light gradient boosting machine, support vector machine, and adaptive boosting—were implemented and compared. The results indicate that the RF model achieved the best predictive performance, outperforming several competing models. To enhance the model’s interpretability, explainable artificial intelligence (XAI) techniques were applied to reveal the interaction of key predictors influencing workers’ behaviors. The results demonstrate that safety communication, risk perception, and supportive work environment are the most influential determinants shaping safety behavior. As a key novelty, this study introduces an ML-based approach for predicting construction workers’ safety behavior and applies XAI techniques to systematically interpret the key determinants of safety behavior. The results also provide valuable insights for safety managers and offer data-driven guidance to enhance the effectiveness of safety interventions.

Natural ventilation remains a key strategy for improving indoor environmental quality (IEQ), lowering energy demand, and increasing resilience in low-rise residential buildings, especially in warm climates where mechanical ventilation is costly or unreliable. Classical ventilation studies are very often performed on computational fluid dynamics (CFD) or simplified thermal models, but they are computationally resource-heavy, data-dependent, or at odds with early design scenarios. Thus, this study proposes a Multi-Scale BIM-Driven Framework for Predictive Ventilation Opportunity Mapping (PVOM), presenting a geometry-based, data-light approach for investigating ventilation potential over micro-, meso-, and macro-scale spatial dimensions. Based on BIM models of two single-story residential buildings (Building A—author-developed and Building B—public reference model), the framework combines LOD 300 spatial modeling, multi-scale ventilation morphometrics, pathway prediction, and design optimization via opening repositioning, resizing, and envelope porosity adjustments. The outcomes indicate that PVOM correctly detects airflow constraints, stagnation pockets, and underperforming spaces, while simultaneously identifying geometrical areas for improvement on cross-ventilation. Performance for optimization scenarios indicated enhanced air change potential (ACH-P), cross-ventilation score (CVS), and spatial airflow continuity (SAC), thereby indicating the framework is adequate in facilitating early-stage sustainable design. This study presents a reproducible BIM-based method on natural ventilation assessment without CFD or advanced sensing systems, indicating PVOM as a scalable approach toward architects, engineers, and sustainability practitioners. BIM; natural ventilation; PVOM; ventilation morphometrics; low-rise buildings; sustainable design; performance optimization.