Work

Attention-Enhanced Progressive Transfer Learning for Scalable Seismic Vulnerability Assessment of RC Frame Buildings

Year: 2025

Type: article

Abstract: Urban infrastructure in seismic zones demands efficient and scalable tools for damage prediction. This study introduces an attention-integrated Progressive Transfer Learning (PTL) framework for seismic vulnerability assessment (SVA) of reinforced concrete (RC) frame buildings. Traditional simulation-based vulnerability models are computationally expensive and dataset-specific, limiting their adaptability. To address this, we leverage a pre-trained artificial neural network (ANN) model based on nonlinear static pushover analysis (NSPA) and Monte Carlo simulations for a 4-story RC frame, and extend its applicability to 2-, 8-, and 12-story configurations via PTL. An attention mechanism is incorporated to prioritize critical features, enhancing interpretability and classification accuracy. The model achieves 95.64% accuracy across five damage categories and an R² of 0.98 for regression-based damage index predictions. Comparative evaluation against classical and deep learning models demonstrates superior generalization and computa-tional efficiency. The proposed framework reduces retraining needs, adapts across ty-pologies, and maintains high predictive fidelity, making it a practical AI solution for structural risk evaluation in seismically active regions.

Source: Buildings

DOI: 10.3390/buildings15234383

Authors: Kaushik M. Gondaliya, Konstantinos Daniel Tsavdaridis, Aanal Raval, Jignesh A. Amin, Komal Borisagar

Institutions: Gujarat Technological University, University of London, City, University of London, City St George's, University of London

Language: English

Topic: Seismic Performance and Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137