A Hybrid CNN–LSTM Framework for Vibration-Based Multi-Damage Assessment in Reinforced Concrete Bridges
TL;DR
A hybrid one-dimensional convolutional neural network and long short-term memory framework for vibration-based damage localisation and severity estimation in reinforced concrete bridges and results support the use of the proposed framework for screening-level assessment of bridge damage under controlled simulated conditions.
Abstract
Structural health monitoring (SHM) is essential for assessing the safety and serviceability of bridge structures. Identifying progressive and concurrent damage remains challenging due to the complex and continuous nature of structural deterioration. This study proposes a hybrid one-dimensional convolutional neural network and long short-term memory (1D-CNN–LSTM) framework for vibration-based damage localisation and severity estimation in reinforced concrete bridges. Operational modal analysis is applied to field-measured vibration data from an in-service bridge. A finite element model is updated using particle swarm optimisation, reducing frequency discrepancies from 7–17% to within ±3%. Progressive single-, double-, and triple-element damage scenarios are simulated through systematic stiffness degradation. The resulting modal frequency data are used to train 1D-CNN–LSTM models using Pareto front optimisation. The proposed framework achieves coefficients of determination above 0.80 with low prediction errors (MSE and MAE < 2) for single- and double-element damage scenarios. The results support the use of the proposed framework for screening-level assessment of bridge damage under controlled simulated conditions.