Quantitative Assessment and Prediction for Tower Crane Construction Safety Resilience Based on Historical Database

This study proposes an equipment-level framework for quantifying and grading tower-crane construction safety resilience that addresses three persistent gaps in construction safety research: subjective weighting, static scoring, and weak uncertainty treatment. The Entropy Weight Method (EWM) with Monte Carlo Simulation (MCS) is integrated to convert five objective indicators (fatalities, serious injuries, economic losses, accident-severity factor, and accident frequency) into (i) data-driven weights and (ii) interval-valued resilience estimates (mean and 95% CI). A quintile scheme yields an interpretable five-tier scale from Very Weak to Very Strong. On a multi-source dataset of 696 accidents, casualties and severity dominate the entropy weights and effectively separate resilience tiers. The MCS intervals are stable and decision-oriented. Using the obtained tiers as labels, a Random-Forest classifier achieves superior Accuracy and Macro-F1, demonstrating that the grading is predictable and thus operational for early warning. Two lightweight proxies were further introduced, the Management Behavior Index (MBI) and the Recovery Difficulty Index (RDI), to incorporate management/behavioral signals and recovery burden; both couple with the EWM-MCS score at small weights, smooth zero-event cases, and highlight priority risks. Sensitivity checks on binning rules, simulation budgets, perturbation magnitudes, and coupling coefficients confirm robustness. The proposed framework generates interconnected output metrics, including the mean value, confidence interval, risk tier, and result interpretability. Furthermore, it exhibits high portability and can be readily adapted to other types of critical construction equipment as well as online assessment workflows.