An entropy-based multi-criteria approach for intensity measure selection in seismic resilience of structures

Seismic resilience (SR) has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure, recover from, and adapt to seismic events. This study presents an entropy-based multi-criteria approach for selecting optimal intensity measures (IMs) to assess SR of structures. Eight representative IMs, derived from time histories and response spectrum are evaluated. Incremental dynamic analysis is conducted on a reinforced concrete structure, using engineering demand parameters such as the maximum inter-story drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model. The optimal IMs are identified through a multi-criteria decision-making process, with scores calculated using the entropy weight method to incorporate factors such as efficiency, proficiency, and uncertainty based on information entropy. An effective SR framework is derived from fragility results. The findings indicate that peak ground velocity and spectral IMs are the most effective, while energy-related IMs underestimate SR. The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments. The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.