A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks
Source abstract
Abstract
In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).
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Research context
OpenAlex topics: Structural Health Monitoring Techniques · Infrastructure Maintenance and Monitoring · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Engineering, Chongqing College of Humanities, Science & Technology, Chongqing 401524, China
School of Business, Chongqing College of Humanities, Science & Technology, Chongqing 401524, China
School of Civil Engineering, Chongqing University, Chongqing 400045, China
Funding
the Scientific Research Program of Chongqing College of Humanities, Science & Tech-nology · the Science and Technology Research Program of Chongqing Municipal Education Commission · Foundation project of Scientific Research Platform of Hechuan for Chengdu-Chongqing Economic Area Research Base and the Teaching Reform Project of School · the Scientific Research Program of Chongqing College of Humanities, Science & Technology
References (37)
Show source-provided references
1. Structural Health Monitoring of Timber Structures—Review of Available Methods and Case Studies10.1016/j.conbuildmat.2020.118528 ↗
2. Rahul, V., Alokita, S., Jayakrishna, K., Kar, V.R., Rajesh, M., Thirumalini, S., and Manikandan, M. (2019). Structural health monitoring of aerospace composites. Structural Health Monitoring of Biocomposites, Fibre-Reinforced Composites and Hybrid Composites, Elsevier.10.1016/B978-0-08-102291-7.00003-4 ↗
3. Xiang, J., Yang, Z., and Aguilar, J. (2018). Structural Health Monitoring for Mechanical Structures Using Multi-Sensor Data. Int. J. Distrib. Sens. Netw., 14.10.1177/1550147718802019 ↗
4. Structural Damage Identification via Physics-Guided Machine Learning: A Methodology Integrating Pattern Recognition with Finite Element Model Updating10.1177/1475921720927488 ↗
5. A Numerical Study on Multi-site Damage Identification: A Data-driven Method via Constrained Independent Component Analysis10.1002/stc.2583 ↗
6. Machine Learning Algorithms for Damage Detection: Kernel-Based Approaches10.1016/j.jsv.2015.11.008 ↗
7. Measurement of Unsteady Aerodynamic Force on a Galloping Prism in a Turbulent Flow: A Hybrid Aeroelastic-Pressure Balance10.1016/j.jfluidstructs.2021.103232 ↗
8. A Perspective on the Aerodynamics and Aeroelasticity of Tapering: Partial Reattachment10.1016/j.jweia.2021.104590 ↗
9. Structural Health Monitoring and Damage Assessment Using a Novel Time Series Analysis Methodology with Sensor Clustering10.1016/j.jsv.2010.09.024 ↗
10. On Assessing the Robustness of Structural Health Monitoring Technologies10.1177/1475921712451956 ↗
11. A Two-Step Method for Damage Identification in Moment Frame Connections Using Support Vector Machine and Differential Evolution Algorithm10.1016/j.asoc.2019.106008 ↗
12. Structural Damage Identification Based on Bayesian Theory and Improved Immune Genetic Algorithm10.1016/j.eswa.2011.12.023 ↗
13. Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems10.1109/TMECH.2020.3009449 ↗
14. Artifacts of Different Dimension Reduction Methods on Hybrid CNN Feature Hierarchy for Hyperspectral Image Classification10.1016/j.ijleo.2021.167757 ↗
15. A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples10.1016/j.neucom.2021.03.035 ↗
16. Lauriola, I., Lavelli, A., and Aiolli, F. (2021). An Introduction to Deep Learning in Natural Language Processing: Models, Techniques, and Tools. Neurocomputing.10.1016/j.neucom.2021.05.103 ↗
17. Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization10.1061/(ASCE)CP.1943-5487.0000820 ↗
18. Convolutional Neural Network-Based Data Anomaly Detection Method Using Multiple Information for Structural Health Monitoring10.1002/stc.2296 ↗
19. 1-D CNNs for Structural Damage Detection: Verification on a Structural Health Monitoring Benchmark Data10.1016/j.neucom.2017.09.069 ↗
20. Vibration-based Structural State Identification by a 1-dimensional Convolutional Neural Network10.1111/mice.12447 ↗
21. Structural Damage Detection and Localization Using a Hybrid Method and Artificial Intelligence Techniques10.1177/1475921719887768 ↗
22. Amini Tehrani, H., Bakhshi, A., and Akhavat, M. (2017). An Effective Approach for Structural Damage Localization in Flexural Members Based on Generalized S-Transform. Sci. Iran.10.24200/sci.2017.20019 ↗
23. Nguyen, T.Q. (2021). A Data-Driven Approach to Structural Health Monitoring of Bridge Structures Based on the Discrete Model and FFT-Deep Learning. J. Vib. Eng. Technol.10.1007/s42417-021-00343-5 ↗
24. Truchetet, F., and Laligant, O. (2004). Comparison of Wavelet- and FFT-Based Single-Channel Speech Signal Noise Reduction Techniques, International Society for Optics and Photonics.
25. Wavelet and FFT Based Image Denoising Using Non-Linear Filters10.11591/ijece.v5i5.pp1018-1026 ↗
26. Zheng, J., Liao, L., and Chen, Y. (2020). Damage Identification Based on Convolutional Neural Network and Recurrence Graph for Beam Bridge. Struct. Health Monit.10.1177/1475921720916928 ↗
27. One-Dimensional Convolutional Neural Networks for Spectroscopic Signal Regression: Feature Extraction Based on 1D-CNN Is Proposed and Validated10.1002/cem.2977 ↗
28. Structural Damage Detection Using Hybrid Deep Learning Algorithm10.31814/stce.nuce2020-14(2)-05 ↗
29. Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties10.1007/s12205-018-1301-2 ↗
30. Experimental Phase II of the Structural Health Monitoring Benchmark Problem
31. Structural Damage Detection with Automatic Feature-Extraction through Deep Learning: Structural Damage Detection with Automatic Feature-Extraction through Deep Learning10.1111/mice.12313 ↗
32. Towards Dropout Training for Convolutional Neural Networks10.1016/j.neunet.2015.07.007 ↗
33. Fan, Z., Li, C., Chen, Y., Mascio, P.D., Chen, X., Zhu, G., and Loprencipe, G. (2020). Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement. Coatings, 10.10.3390/coatings10020152 ↗
34. Data-Driven Support Vector Machine with Optimization Techniques for Structural Health Monitoring and Damage Detection10.1007/s12205-017-1518-5 ↗
35. Lei, X., Sun, L., Xia, Y., and He, T. (2020). Vibration-Based Seismic Damage States Evaluation for Regional Concrete Beam Bridges Using Random Forest Method. Sustainability, 12.10.3390/su12125106 ↗
36. Vitola, J., Pozo, F., Tibaduiza, D., and Anaya, M. (2017). A Sensor Data Fusion System Based on K-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications. Sensors, 17.10.3390/s17020417 ↗
37. Leon-Medina, J.X., Anaya, M., Parés, N., Tibaduiza, D.A., and Pozo, F. (2021). Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting. Sensors, 21.10.3390/s21082748 ↗