Work

Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning

Year: 2017

Type: article

Abstract: Structural damage detection is still a challenging problem owing to the difficulty of extracting damage-sensitive and noise-robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low-level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise-free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1111/mice.12313

Authors: Yi‐zhou Lin, Zhen‐hua Nie, Hong‐wei Ma

Institutions: Jinan University, Qinghai University, Dongguan University of Technology

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,230