Human–Machine System Modeling and Safety Risk Assessment in Construction Operations Incorporating Workers’ Performance Variability

Abstract

The increasing involvement of diverse construction machines has significantly transformed construction operations. Within the nonlinear interaction of multiple worker and machine functions, workers’ performance exhibits complex variability due to cognitive uncertainty and contextual correlation. These factors have a profound impact on the construction operation process, leading to the emergence of new and intricate risk patterns. Adopting a human–machine system perspective, investigating the influence of workers’ performance variability (WPV) on construction operation processes holds great potential for enhancing system safety. This study introduces an integrated method merging a Bayesian network (BN)-based cognitive model with the functional resonance analysis method (FRAM). The BN model estimates the probability distribution of WPV manifestations. Subsequently, Bayesian inference and FRAM-based quantitative analysis are consolidated within a Monte Carlo simulation framework to evaluate the WPV’s influence on system safety. The efficacy of this approach was evaluated by conducting a shield tunneling construction case study, which revealed that the proposed method could identify critical types of WPV, assess their impacts, and explain their causes. The contribution of this study lies in providing an appropriate modeling and analysis method for the intricately functionally coupled worker–machine collaborative construction operation process and its associated risks. The proposed approach has the potential to support the development of practical tools that offer quantitative evidence for managing WPV and mitigating its adverse effects on system safety. The modern construction industry is experiencing an increasing prevalence of human–machine collaborative construction scenarios, in which construction workers and machines form tightly coupled systems. Understanding the safety risks in such complex human–machine systems has become increasingly crucial. Furthermore, as construction machinery becomes more reliable and exhibits relatively certain performance, it is essential to comprehend the dominant role played by human workers and how the uncertainty of their behavior impacts the construction human–machine system safety. Based on the Safety-II theory, this study offers a distinct perspective for understanding the risks of construction human–machine systems. It not only considers those situations in which failures may occur, but also explores those situations in which construction tasks are completed successfully, which is important for understanding the complex task risks inherent in human–machine systems. The results demonstrate the effects of macro task processes and the micro worker’s performance variability. The findings reveal the complex dynamics of such systems and provide insights into both potential failure scenarios and successful task completion, ultimately enhancing risk assessment practices. The proposed methodology also holds promise as a tool for integrating expert experience and construction process data, enabling proactive analysis of risks in construction human–machine systems.