Identifying effective management factors across human errors – A case in elevator installation

Abstract

Human errors are recognized as the main factor in causing construction accidents. Previous studies mainly focused on justifying theories associated with human errors and hardly quantifying the causal relations between external stimuli and human errors. Hence, the aim of this research is to develop comprehensive management measurements for addressing human errors in construction projects. Deductive reasoning was used to describe the methodological process. Firstly, we constructed a theoretical model of human errors based on Cognitive Reliability and Error Analysis Method (CREAM) and knowledge-combined structure learning algorithm. Then, Bootstrap method was adopted to verify the reliability of network topology, while the similarity-flooding algorithm was used to analyze similarity of factors across various causal models for their commonalities. Subsequently, Bayesian parameter estimation was to analyze the sensitivity of the nodes. The results show that inadequate quality control, design failure and inattention are the most fundamental causes of human errors in promoting safety management. This research has proposed an analytical approach that consolidated the influential mechanics to reflect the overall influence of a root cause in the human error. Ultimately, the research lays an analytical foundation for safety management research in the future.