An Integrated Resilience Analysis Framework for Highway Tunnel Engineering Systems Using N-K-ISM Models Based on Decoupling thinking Shenyang Urban Construction University, School of Management

Format:

Book

Conference/Event

Author/Creator:

Wang, Chunyu, author.

Contributor:

An, Jingru

Conference Name:

2025 International Conference on Transportation Infrastructure and Engineering (ICTIE2025) (2025-06-06 : Guilin, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Before Highway tunnel engineering is a complex system undergoing various evolutionary stages and characterized by multiple risk factors. The increasing interconnection and coupling of these risk factors can lead to operational accidents or disruptive events. These coupling effects pose significant challenges for project managers in effectively managing highway tunnel systems. Traditional risk-centered analysis approaches, which focus on post-event effects and causes while paying less attention to the coupling effects among risk factors, inadequately address these challenges. To fill this gap, this study examined the resilience evolution mechanism from all life cycle perspective and proposed a multi-factor and multi-stage resilience analysis framework. This integrated framework integrates the Natural Killing (N-K) model and the Interpretive Structural Model (ISM) to analyze coupling utility and implement decoupling control of resilience factors. The N-K model measures the coupling utility of three types of resilience factors: single, dual, and multi-factor. The output of this coupling effect is applied to the ISM model to determine decoupling strategies to control coupling risks based decoupling thinking. Finally, taking 112 major operational accidents in highway tunnel engineering as an empirical study, both domestically and internationally, from the perspectives of personnel, equipment, environment, and management. The results indicate that the risk coupling value of the resistance subsystem is the highest, followed by the recovery subsystem. In the resistance and recovery stages, the system is most likely to fail when disturbed, with personnel and management factors identified as the root causes

Notes:

Vendor supplied data

Publisher Number:

2025-99-0364

Access Restriction:

Restricted for use by site license