A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure
Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration respo...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-02-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/4/1059 |
id |
doaj-28725beb3a4944cd9b17de774cd0ff02 |
---|---|
record_format |
Article |
spelling |
doaj-28725beb3a4944cd9b17de774cd0ff022020-11-25T02:16:09ZengMDPI AGSensors1424-82202020-02-01204105910.3390/s20041059s20041059A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark StructureTongwei Liu0Hao Xu1Minvydas Ragulskis2Maosen Cao3Wiesław Ostachowicz4Department of Engineering Mechanics, Hohai University, Nanjing 210098, ChinaSchool of Aeronautics and Astronautics, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, ChinaCenter for Nonlinear Systems, Kaunas University of Technology, Studentu 50-146, LT-51368 Kaunas, LithuaniaDepartment of Engineering Mechanics, Hohai University, Nanjing 210098, ChinaInstitute of Fluid-Flow Machinery, Polish Academy of Sciences, 80-231 Gdansk, PolandVibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)-based frequency-domain signals, the TF signals exhibited more significant damage-sensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, high-dimensional datasets. The developed TF-1D CNN damage identification framework was demonstrated to have practical value in future applications.https://www.mdpi.com/1424-8220/20/4/1059structural health monitoringdamage identificationtransmissibility functionconvolutional neural networksdeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tongwei Liu Hao Xu Minvydas Ragulskis Maosen Cao Wiesław Ostachowicz |
spellingShingle |
Tongwei Liu Hao Xu Minvydas Ragulskis Maosen Cao Wiesław Ostachowicz A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure Sensors structural health monitoring damage identification transmissibility function convolutional neural networks deep learning |
author_facet |
Tongwei Liu Hao Xu Minvydas Ragulskis Maosen Cao Wiesław Ostachowicz |
author_sort |
Tongwei Liu |
title |
A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure |
title_short |
A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure |
title_full |
A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure |
title_fullStr |
A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure |
title_full_unstemmed |
A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure |
title_sort |
data-driven damage identification framework based on transmissibility function datasets and one-dimensional convolutional neural networks: verification on a structural health monitoring benchmark structure |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-02-01 |
description |
Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)-based frequency-domain signals, the TF signals exhibited more significant damage-sensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, high-dimensional datasets. The developed TF-1D CNN damage identification framework was demonstrated to have practical value in future applications. |
topic |
structural health monitoring damage identification transmissibility function convolutional neural networks deep learning |
url |
https://www.mdpi.com/1424-8220/20/4/1059 |
work_keys_str_mv |
AT tongweiliu adatadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT haoxu adatadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT minvydasragulskis adatadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT maosencao adatadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT wiesławostachowicz adatadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT tongweiliu datadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT haoxu datadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT minvydasragulskis datadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT maosencao datadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure AT wiesławostachowicz datadrivendamageidentificationframeworkbasedontransmissibilityfunctiondatasetsandonedimensionalconvolutionalneuralnetworksverificationonastructuralhealthmonitoringbenchmarkstructure |
_version_ |
1724892413044981760 |