Comprehensive lifecycle safety risk assessment for construction robotics using T-S fault tree analysis and Bayesian network

Abstract

The application of construction robots introduces unprecedented safety challenges, underscoring a research gap in safety risk assessment throughout the application processes. This paper focused on the lifecycle safety risks associated with the entry, debugging, operation, maintenance, and exit phases of construction robots, identifying 13 risk categories and 52 risk factors. Moreover, Takagi and Sugeno fault tree analysis (TS-FTA) and Bayesian network were integrated to establish risk assessment models based on accident type analysis, indicating environmental failures and unsafe management behaviors as critical in electrical accidents, while human and physical failures are predominant in mechanical injuries. The results underscore unique risk manifestations and management priorities, emphasizing the importance of addressing emerging risks and prioritizing resources for critical risks such as insufficient on-site safety risk control, inadequate emergency management, and cluttered environment. This paper offers a comprehensive framework for risk assessment and management in construction robot applications, contributing to safer project execution.