Data-Driven Early Warning of Human-Machine Collisions Via Computational Intelligence: a Task Demand-Capacity Equilibrium Framework for Proactive Construction Safety

Industrial environments involve complex human-machine collaboration, where behavioral variability and dynamic task interactions introduce safety risks that traditional rule-based or distance-based approaches cannot effectively capture. Existing systems typically rely on spatial thresholds or equipment states, lacking the datadriven intelligence needed to explain and predict unsafe behaviors in real time. To address these limitations, this study develops an intelligent framework for proactive safety management by introducing a task demand-capacity equilibrium model. Drawing on an organizational perspective from management science, this framework conceptualizes unsafe behavior as a system-level misalignment between task requirements and collective work crew configurations. This approach integrates computer vision and ultra-wideband (UWB) sensing for automated role recognition and employs unsupervised clustering to derive adaptive thresholds for risk detection. This research reveals several key insights. First, shifting the analytical unit from the individual operator to the collective work crew enables the capture of nonlinear dynamics between workforce size and safety capacity, significantly enhancing the interpretability of behavioral risk emergence. Second, the clusteringbased strategy enables the autonomous identification of recurrent work crew patterns without reliance on rigid rules, allowing the system to adaptively evolve with changing task demands and site variability. Finally, field validation in mobile crane operations yielded a precision of 91.68%, demonstrating the feasibility of transforming real-time sensing data into actionable safety knowledge. This research thus proposes a methodological digital framework for intelligent risk analytics, providing a reference for AI-enabled decision-support systems to utilize operational data to enhance proactive prevention and adaptive safety management in relevant safety-critical industries.