AI-supported seismic performance evaluation of structures: challenges, gaps, and future directions at early design stages

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Authors: Fatma Ak, Berk Ekici, Uğur Demir
Cited by: 2
References: 123

Abstract

This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.

Fields of Study & Subjects

Seismic Performance and Analysis BIM and Construction Integration Topology Optimization in Engineering

Publication details

Journal / source
Advanced Engineering Informatics
Publisher
Elsevier BV
Volume
71
Pages
104301–104301
Publication date
30 January 2026
Language
EN