A Multi-Cohort Weighted Calibration Approach to Rectifying Subjective Bias in Construction Site LEC Hazard Identification

Traditional Likelihood-Exposure-Consequence (LEC) safety evaluations suffer from pronounced subjective variance and cognitive bias in construction site risk management. To overcome the methodological limitations, this study establishes a multi-cohort empirical framework analyzing 120 validated assessments across eight high-frequency construction cases evaluated by safety domain experts (SDE), frontline operational workers (FOW), and non-specialist personnel (NSP). Statistical decomposition of individual risk parameters demonstrates that accident likelihood ($L$ ) and exposure frequency ($E$ ) maintain strong baseline consensus across cohorts, whereas consequence severity ($C$ ) acts as the primary driver of subjective rating variance, which is nonlinearly amplified through the multiplicative risk score $D=L\times E\times C$ . Frontline operational workers consistently underestimate high-hazard risks owing to habituation bias, whereas non-specialist personnel display extreme risk polarization due to domain knowledge deficits. In order to rectify the systemic cognitive distortions, situation-specific calibration coefficients ($k=D_{expert}/D_{evaluated}$ ) were mathematically derived. Empirical results demonstrate that overestimated hazards require scaling factors below 0.8 ($k<0.8$ ) to compress inflated scores, while severely underestimated operations such as scaffold erection require scaling factors exceeding 1.4 ($k>1.4$ ) to restore risk ratings to expert reference levels. Integrating multi-stakeholder consensus with dynamic calibration matrices transforms subjective field experience into a standardized risk