Work

Rapid post-earthquake assessment of bridge portfolios using damage-state augmentation

Year: 2026

Type: article

Abstract: Rapid portfolio-level post-earthquake bridge damage assessment is essential for restoring transportation networks and supporting emergency decisions, yet the current approaches face two persistent barriers. High-fidelity nonlinear analyses are computationally prohibitive at portfolio scale, while data-driven classifiers have been hindered by scarce and severely imbalanced damage-state labels, especially for severe damage. To address these gaps, this study presents a computational model consisting of two coupled components: a recurrent neural network surrogate for efficient response prediction and a gradient boosting-based classifier for damage-state identification. The surrogate learns the mapping from ground-motion features and bridge parameters to curvature-related response quantities, which are then used as physically meaningful features for classification. The classifier prioritizes the informative samples based on the predictive uncertainty and incorporates imbalance-mitigation strategies during training. Across five representative bridge types, the proposed model achieves test accuracies of 87.5%-96.5% for type-specific training and 83.1%-89.8% for joint cross-type training, demonstrating strong cross-type generalization.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1016/j.cacaie.2026.100061

Authors: Shengkui Di, Yaoyue Wang, Dong Yang, Francis T.K. Au, Yanhui Liu, Jing Zhang

Institutions: Lanzhou Jiaotong University, Guangzhou University, University of Hong Kong, Jinan University

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: hybrid

Estimated APC paid: $5,230