Neural network analysis of construction safety management systems: a case study in Singapore

Abstract

A neural network analysis was conducted on a quantitative occupational safety and health management system (OSHMS) audit with accident data obtained from the Singapore construction industry. The analysis is meant to investigate, through a case study, how neural network methodology can be used to understand the relationship between OSHMS elements and safety performance, and identify the critical OSHMS elements that have significant influence on the occurrence and severity of accidents in Singapore. Based on the analysis, the model may be used to predict the severity of accidents with adequate accuracy. More importantly, it was identified that the three most significant OSHMS elements in the case study are: incident investigation and analysis, emergency preparedness, and group meetings. The findings imply that learning from incidents, having well-prepared consequence mitigation strategies and open communication can reduce the severity and likelihood of accidents on construction worksites in Singapore. It was also demonstrated that a neural network approach is feasible for analysing empirical OSHMS data to derive meaningful insights on how to improve safety performance. © 2013 Copyright Taylor and Francis Group, LLC.