Next Article in Journal
Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
Previous Article in Journal
Zero Energy in the Built Environment: A Holistic Understanding
Article Menu

Export Article

Open AccessArticle

Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks

1
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore 999002, Singapore
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3376; https://doi.org/10.3390/app9163376
Received: 24 July 2019 / Revised: 10 August 2019 / Accepted: 13 August 2019 / Published: 16 August 2019
(This article belongs to the Section Civil Engineering)
  |  
PDF [5231 KB, uploaded 16 August 2019]
  |     |  

Abstract

In this paper, a convolutional neural network (CNN) was used to extract the damage features of a steel frame structure. As structural damage could induce changes of the modal parameters of the structure, the convolution operation was used to extract the features of modal parameters, and a classification algorithm was used to judge the damage state of the structure. The finite element method was applied to analyze the free vibration of the steel frame and obtain the first-order modal strain energy for various damage scenarios, which was used as the CNN training sample. Then vibration experiments were carried out, and modal parameters were obtained from the modal analysis of the vibration signals. The experimental data were inputted into the CNN to verify its damage detection capability. The result showed that the CNN was effective in detecting the intact structure, single damage, and multi damages with an accuracy of 100%. For comparison, the same samples were also applied to the traditional back propagation (BP) neural network, which failed to detect the intact structure and multiple-damage cases. It was found that: (1) The proposed CNN could be trained from finite element simulation data and used in real frame structure damage detection, and it performed better in structural damage detection than BP neural networks; (2) the measured data of a real structure could be supplemented by numerical simulation data, and satisfactory results have been demonstrated. View Full-Text
Keywords: structural damage detection; convolutional neural networks; modal strain energy; steel frame; finite element method; vibration experiment structural damage detection; convolutional neural networks; modal strain energy; steel frame; finite element method; vibration experiment
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Teng, S.; Chen, G.; Liu, G.; Lv, J.; Cui, F. Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks. Appl. Sci. 2019, 9, 3376.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top