An artificial intelligence model for rapid health assessment of earthquake-affected shear wall buildings: a disaster mitigation study

Abstract

Rapid post-earthquake health assessment of shear wall buildings is critical for effective disaster mitigation and swift rehabilitation, particularly in earthquake-affected urban areas. This study developed a novel artificial intelligence (AI)-based approach to predict key structural health parameters—maximum displacement, maximum inter-storey drift (MISD), base shear, and maximum overturning moment—without requiring physical measurements of structural response. Instead of instrumented monitoring, the model relies solely on earthquake ground motion records that are readily available online, enabling fast and reliable predictions. A robust AI model was trained on a curated dataset generated from validated finite element simulations, attaining a high regression accuracy of 97.528%. The approach captures a broad spectrum of ground motion frequencies and structural response parameters, ensuring adaptability to diverse seismic conditions and urban settings. The results demonstrate that the AI model can efficiently assess structural integrity for disaster mitigation, providing a cost-effective and non-intrusive post-earthquake assessment solution. Furthermore, the methodology can be extended to other building types, paving the way for a generalized AI-driven structural health assessment system. This research highlights the transformative potential of AI to enhance the speed, accuracy, and practicality of seismic risk assessment for high-rise shear wall buildings in disaster mitigation.

Keywords

Structural health monitoring (SHM) / Time history analysis / Earthquake ground motions / Artificial intelligence (AI) / Shear wall buildings / Finite element models / Concrete shear wall

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