Dynamic resilience assessment and multi-objective optimization decision-making for urban roadway tunnel system in the face of fire disaster

Abstract

The urban roadway tunnel system (URTS), as an infrastructure system that includes equipment and facilities, operational staff, and traffic participants, faces challenges arising from various potential fire threats. Existing studies on tunnel fire risk primarily focus on static assessment, neglecting dynamic changes over time, and insufficiently considering the complexity of tunnel composition, leading to incomplete identification of influential factors. Additionally, few studies were conducted to develop optimal operation and maintenance (O&M) strategies under cost constraints. To bolster fire safety management of URTS, a fire framework that combines resilience assessment and optimization is proposed based on system resilience theory, Bayesian network (BN), and multi-objective optimization (MOPT) in this paper. The framework is applied to Hangzhou’s URTS. The results indicate that Hangzhou’s URTS has a current “Medium” fire resilience level of 0.640, decreasing to 0.568 in 20 years without scientific O&M. The static and dynamic strategies are acquired through sensitivity and critical importance analysis, enhancing long-term fire resilience. Moreover, optimal strategies for varied investments in diverse periods are explored, considering O&M costs and resilience levels. The fire resilience framework introduced herein can integrate into various infrastructure systems, effectively enhancing disaster resilience and promoting sustainable development.