An Integrated Resilience and Assessment Methodology Framework for Addressing Uncertainty in Highway Operation and Maintenance Systems

Abstract

Highway operation and maintenance (O&M) systems exhibit characteristics of multisubsystem correlations and interactions among multiple disaster-causing factors over time. Traditional risk-centered approaches, which focus on postevent effects and causes, fail to address these challenges adequately. This study aims to devise a methodological framework that is multisubsystem and multifactor dynamic in nature, intended for assessing, analyzing, and enhancing the resilience of highway O&M systems. This framework comprises a qualitative component, utilizing fault tree analysis (FTA) to identify resilience factors within each subsystem, and a quantitative aspect, employing the improved human factors analysis and classification system (HFACS) and fuzzy dynamic Bayesian networks (FDBNs) for resilience assessment. Subsequently, a case study of the Shenyang-Dalian Highway (SDH) in China was employed to demonstrate the application of this methodological framework. The findings highlight the need for enhancing the resilience of highway O&M systems, particularly by focusing on personnel and equipment factors within the absorption and resistance subsystems and prioritizing organizational and personnel factors within the recovery and adaptation subsystems. Furthermore, dynamic strategies tailored to different O&M objectives were proposed. These frameworks contribute to the existing knowledge base of highway O&M systems by integrating resilience theory into performance evaluation and improvement efforts amid uncertainty.