Structural Damage Detection Using Convolutional Neural Networks Based on Modal Strain Energy and Population of Structures-Reference-Cited by-同舟云学术

Structural Damage Detection Using Convolutional Neural Networks Based on Modal Strain Energy and Population of Structures

Published:2022-12-05 Issue:03 Volume:20 Page:
ISSN:0219-8762
Container-title:International Journal of Computational Methods
language:en
Short-container-title:Int. J. Comput. Methods

Author:

Zhang Jiqiao¹,Jin Zihan¹,Teng Shuai¹,Chen Gongfa¹,Cui Fangsen²

Affiliation:

1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, P. R. China

2. Institute of High Performance Computing, A*STAR (Agency for Science, Technology and Research), Singapore 138632, Republic of Singapore

Abstract

A convolutional neural network (CNN)-based structural damage detection (SDD) method using populations of structures and modal strain energy (MSE) is proposed. In this study, sufficient samples of the CNN are provided by numerical simulations, and the size of the model can be changed by modifying the coordinates of some nodes, thereby establishing a series of numerical models (i.e., a population). Finally, three groups are investigated, the effects of multiple indices on damage detection based on population are compared. The results demonstrate that the MSE as a damage index is superior to the other indices.

Funder

Guangdong Province High Level University Construction for the Guangdong University of Technology

College Student Innovation Training Program of the Guangdong University of Technology

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computational Mathematics,Computer Science (miscellaneous)

Link

https://www.worldscientific.com/doi/pdf/10.1142/S021987622230001X

Reference40 articles.

1. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

2. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

3. Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

4. Avci, O. et al. [2002] A New Benchmark Problem for Structural Damage Detection: Bolt Loosening Tests on a Large-Scale Laboratory Structure (Cham, New York) pp. 15–22.

5. Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization

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