Machine-learning seismic damage assessment model for building structures

Abstract

Buildings in seismic-prone regions are highly vulnerable to structural damage, necessitating meticulous Seismic Damage Assessment (SDA) for accurate design and mitigation strategies. The intricate nature of Seismic Damage Assessment (SDA) poses challenges, particularly when employing Finite Element Analysis (FE) for individual structures, as simulation techniques are time-intensive due to the inherent complexity of the models. Computational methods combining Soil-Structure Interaction (SSI) for earthquake damage assessment further compound the challenge, requiring substantial computational efforts to construct a comprehensive database for area-based prediction models. This study introduces such challenges via a novel Artificial Neural Network (ANN) approaches-based model as an alternative for prompt building Seismic Damage Assessment evaluation. The proposed ANN model leverages three key inputs—seismic, building, and soil parameters—incorporating a multi-step analysis process to generate seismic responses with soil-structure interaction. The findings underscore the remarkable accuracy of the SDA-Net model, positioning it as an effective predictive tool and rapid decision support system for structures affected by SSI impacts. This innovative approach not only serves as a proactive pre-disaster management tool for assessing potential damage but also emerges as a practical asset for ensuring the safety and durability of structures in the face of natural disasters. The study's contribution lies in its potential application as a valuable tool in structural engineering, aligning with the objectives and scope of the Research Journal of The Institution of Structural Engineers.