TITLE:
A Multi-Cohort Weighted Calibration Approach to Rectifying Subjective Bias in Construction Site LEC Hazard Identification
AUTHORS:
Mo Shi, Xinyue Cu, Nan Feng, Sanggoo Kang, Xiaoxian Huang
KEYWORDS:
Construction Safety Management, LEC Method, Multi-Cohort Risk Perception, Cognitive Bias Calibration, Situation-Specific Calibration
JOURNAL NAME:
Open Journal of Safety Science and Technology,
Vol.16 No.4,
October
29,
2026
ABSTRACT: 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×E×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 assessment protocol, substantially optimizing safety resource allocation and hazard identification in construction management.