Development of a time-variant causal model of human error in construction with dynamic Bayesian network

Purpose: Human error is among the leading causes of construction-based accidents. Previous studies on the factors affecting human error are rather vague from the perspective of complex and changeable working environments. The purpose of this paper is to develop a dynamic causal model of human errors to improve safety management in the construction industry. A theoretical model is developed and tested through a case study. Design/methodology/approach: First, the authors defined the causal relationship between construction and human errors based on the cognitive reliability and error analysis method (CREAM). A dynamic Bayesian network (DBN) was then developed by connecting time-variant causal relationships of human errors. Next, prediction, sensitivity analysis and diagnostic analysis of DBN were applied to demonstrate the function of this model. Finally, a case study of elevator installation was presented to verify the feasibility and applicability of the proposed approach in a construction work environment. Findings: The results of the proposed model were closer to those of practice than previous static models, and the features of the systematization and dynamics are more efficient in adapting toward increasingly complex and changeable environments. Originality/value: This research integrated CREAM as the theoretical foundation for a novel time-variant causal model of human errors in construction. Practically, this model highlights the hazards that potentially trigger human error occurrences, facilitating the implementation of proactive safety strategy and safety measures in advance.