The purpose of this study is to develop a reliable and rapid method for seismic damage detection in industrial structures using artificial neural networks (ANN). Industrial buildings often lack traditional seismic-resistant elements like shear walls, making them more vulnerable to earthquake-induced damage. Due to the difficulty of visually identifying internal structural damage, this research aims to offer a robust AI-based solution for detecting and evaluating such damage efficiently. The proposed approach facilitates post-earthquake safety assessment and structural integrity evaluation through rapid prediction of critical design parameters.
This study employs a feed-forward multi-layer ANN model trained on earthquake ground motion data and structural response outputs obtained via finite element simulations of a reinforced concrete (RC) industrial building. The ANN maps real-time seismic input parameters to essential structural design variables, such as inter-storey drift and maximum displacement. The model is designed to provide quick and accurate estimations of structural damage, enabling post-earthquake evaluations. The approach integrates numerical modeling and AI to build a predictive tool for structural health monitoring in seismic environments.
The ANN model demonstrated high accuracy and efficiency in predicting key structural responses under seismic loading. The trained network successfully identified critical post-earthquake parameters, such as maximum displacement and inter-storey drift, indicating its suitability for rapid seismic health assessments. Results confirmed the model's capability to localize and quantify potential damage in industrial structures that lack traditional seismic reinforcements. The approach is both time-efficient and cost-effective, showing potential for large-scale implementation in structural health monitoring systems for industrial infrastructure.
This research introduces a novel ANN-based framework tailored specifically for seismic damage detection in industrial structures – a domain where SHM applications are limited. Unlike conventional methods, this approach enables real-time monitoring and rapid post-earthquake evaluation without reliance on labor-intensive inspections. By combining AI techniques with numerical simulations, the model provides a practical solution for enhancing seismic resilience and safety in industrial facilities. Its ability to predict critical structural parameters with minimal computation time offers significant value for emergency response, structural retrofitting decisions and disaster preparedness strategies.
