Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network
The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure.
You must Login to post a comment
How to Cite
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.
Open Access Statement
Frattura ed Integrità Strutturale (Fracture and Structural Integrity, F&SI) is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the DOAI definition of open access.
F&SI operates under the Creative Commons Licence Attribution 4.0 International (CC-BY 4.0). This allows to copy and redistribute the material in any medium or format, to remix, transform and build upon the material for any purpose, even commercially, but giving appropriate credit and providing a link to the license and indicating if changes were made.