Modeling and risk assessment of workers’ situation awareness in human-machine collaborative construction operations: A computational cognitive modeling and simulation approach

Abstract

Insufficient situation awareness (SA) among workers remains a prominent factor contributing to construction accidents in complex and high-risk human-machine collaborative construction operations. However, previous studies have not fully explored the impact of various internal and external factors on the formation of workers’ SA, making it difficult to understand the potential changes in SA and respond to its error risks in specific scenarios. To address this issue, this paper proposes a proactive analysis approach of worker’s SA and the corresponding error risk based on computational cognitive modeling and simulation. This approach establishes a perception model by quantitatively depicting the mechanism underlying workers’ attention formation. Bayesian network is employed to represent the belief propagation process involved in worker’s comprehension and projection of the situation. The Monte Carlo method is applied to dynamically analyze the uncertainty inherent in the formation of workers’ SA. To demonstrate the feasibility and validity of the proposed approach, a shield tunneling construction project was adopted as an example. The results indicate that crucial factors such as stress, mental fatigue, and risk preference significantly impact shield machine operation workers’ SA, revealing dynamic changes and interactions of cognitive components within the SA formation process. The findings suggest that the proposed approach can serve as a proactive analysis tool to offer new insights for predicting and controlling risks associated with workers’ SA errors.