A Novel Bridge Damage Diagnosis Algorithm Based on Deep Learning with Gray Relational Analysis for Intelligent Bridge Monitoring System

Haitao Xiao*, Wenjie Wang, Limeng Dong, Harutoshi Ogai

*この研究の対応する著者

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)730-742
ページ数13
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
16
5
DOI
出版ステータスPublished - 2021 5

ASJC Scopus subject areas

  • 電子工学および電気工学

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