Abstract
Accident models are a critical element of safety science as they provide a profound understanding of accident causality, which helps develop accident prevention and control strategies. The evolving accident situations and complex engineering systems offer significant challenges in understanding causality. This limits the realistic representation of causality in the accident model, impacting its usefulness. This paper presents a framework to demystify the understanding of causality and its mathematical representation in accident modeling. The framework uses the theory of causality, understanding interdependencies, constructing these elements in mathematical representations to formulate a mathematical accident model, and subsequently utilizing advanced probability theory and machine learning for accident analysis. The methodology is demonstrated in the case of the Champlain Tower South collapse. The ultimate objective of this work is that readers can use this framework for any engineering system accident modeling (i.e., construction, road, or process systems). The framework will help develop accident preventive and control strategies.
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