In recent years, intelligent structural damage diagnosis algorithms using machine learning have achieved much success. However, because of the fact that in real bridge applications, the working environment (load, temperature, and noise) is changing all the time, degradation of the performance of intelligent structural damage diagnosis methods is very serious. To address these problems, a novel bridge diagnosis algorithm based on deep learning is proposed. Our contributions include: First, we proposed an improved denoising auto-encoder-based deep neural networks, which is optimized by the gray relational analysis. It is able to automatically extract high-level features from raw signals via a multi-layer extraction to satisfy any damage diagnosis objective and thus does not need any time consuming denoising prepossessing. The model can achieve high accuracy under noisy environment. Second, the algorithm does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working environment is changed. Numerical simulations and experimental investigations on real bridges conducted to present the accuracy and efficiency of the proposed algorithm, comparing with other commonly machine learning-based algorithms. The result shows it is deemed as an ideal and effective method for damage diagnosis of bridge structures.
|ジャーナル||IEEJ Transactions on Electrical and Electronic Engineering|
|出版ステータス||Published - 2021 5月|
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