Intelligent monitoring-based resilience assessment system for prefabricated underground structures based on restorability

Abstract

In the context of low-carbon strategies, China has joined the global leadership series of prefabricated underground structures (PUS). However, PUS faces significant challenges in construction, maintenance, and restoration subjected to multi-hazard impacts, particularly at the assembled joints. Addressing this research gap, this study focuses on the PUS life-cycle resilience based on joint restorability. The full-scale experimental studies were conducted on typical assembled joint of PUS and its restored specimen. Integrating experimental results with intelligent monitoring techniques, a resilience assessment framework for the PUS was proposed. The effectiveness of this framework was validated through a case study. Key findings include: (1) Even with minor restoration level undetected rotations, the assembled joint can rapidly reach major restoration thresholds after cumulative operational impacts. (2) Intelligent monitoring techniques enable rapid and accurate responses to restoration needs. It reduces recovery time and cost by more than 10 times, with a 52 % resilience enhancement compared to traditional techniques. (3) The proposed joint restoration technology enhances the yield and ultimate load-bearing capacities by 11.8 % and 7.9 %, respectively, primarily owing to the enhancement of interface bond strength. (4) Restored joints exhibit higher resilience under subsequent daily operations and extreme conditions. Compared to original assembled joints, restored joints show 29 % improvements in rotation tolerance at minor restoration thresholds. Along with 22 % resilience enhancement, the restored joint significantly reduces more than 90 % restoration costs. This study not only provides key restoration technologies and intelligent resilience management methods for the life-cycle resilience of PUS, but also has significant practical application value and resilience enhancement implications.