Damage diagnosis of bridge structures using deep learning strategies: A hybrid neural networks practical tool

Abstract

The accurate identification of damage in bridge structures is critical for ensuring long-term safety and planning timely maintenance interventions. However, environmental noise significantly challenges traditional vibration-based monitoring techniques. The proposed approach develops a deep learning-based intelligent damage classification framework, where stacked denoising autoencoders are employed for noise reduction, and an Inception-enhanced Convolution Neural Networks together with a bi-directional long short-term memory networks are used to automatically extract discriminative spatio-temporal features and classify structural damage states. The performance of the proposed approach is shown by the analysis of a numerical model of the Hanwu cable-stayed bridge in China and field data from the Z24 bridge in Switzerland. The results demonstrate that the proposed method achieves superior classification accuracy and robustness compared to conventional health monitoring models, particularly under noisy conditions, offering a practical tool for bridge health monitoring. The key innovation of this study lies in the integration of learnable denoising and multi-scale spatio-temporal feature extraction within a unified framework, which reduces reliance on hand-crafted preprocessing and significantly enhances robustness under noisy monitoring conditions.