A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data-Driven SHM Framework
2.2. Filtering Procedure
2.3. Pooling Procedure
2.4. Dataset Format and ML Models
2.5. Optimizing Procedure
3. Experimental Program
3.1. Vehicle Model
3.2. Bridge Model
3.3. Damage Cases
4. Results and Analysis
4.1. Performance Evaluation of the Proposed Algorithm
4.2. Discussion on the Filtering Procedure
4.3. Discussion on the Pooling Procedure
4.4. Discussion on the Sensor Location
5. Conclusions
- (1)
- The present algorithm can effectively improve the accuracy and efficiency of different ML models in damage detection. Compared to using raw data, the average accuracy increased by 12.2–15.0%, and the average efficiency increased by 35.7–96.7% for different damaged cases and ML models. This is of great benefit to the data-driven indirect SHM framework.
- (2)
- The filtering procedure primarily eliminates the noise in the data, which is the high-frequency signal associated with ambient noise in this study. There are optimal window function parameters that may achieve the highest accuracy of ML models, but more than that, the results also show that data smoothing can be beneficial for improving accuracy.
- (3)
- The pooling procedure further reduces noise and lessens data redundancy. Appropriate window lengths can balance the “complexity” and “diversity” of the data to retain the necessary information while removing noise and redundancy; they can greatly improve the performance of ML models.
- (4)
- When the proposed method is applied to process data from both the front axle and the rear axle, a similar accuracy or efficiency improvement can be obtained; the algorithm is not significantly affected by the sensor location.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Infrastructure of America’s. A Comprehensive Assessment of America’s Infrastructure; ASCE: Washington, DC, USA, 2021. [Google Scholar]
- Gkoumas, K.; Marques dos Santos, F.; van Balen, M.; Tsakalidis, A.; Ortega, A.; Grosso, M.; Haq, A.; Pekár, F. Research and Innovation in Bridge Maintenance, Inspection and Monitoring a European Perspective Based on the Transport Research and Innovation Monitoring and Information System (TRIMIS); European Commission: Luxembourg, 2019; ISBN 978-92-76-03380-6. [Google Scholar]
- Mattioli, G. What Caused the Genoa Bridge Collapse—And the End of an Italian National Myth? Available online: https://www.theguardian.com/cities/2019/feb/26/what-caused-the-genoa-morandi-bridge-collapse-and-the-end-of-an-italian-national-myth (accessed on 9 September 2022).
- Invernizzi, S.; Montagnoli, F.; Carpinteri, A. Fatigue Assessment of the Collapsed XXth Century Cable-Stayed Polcevera Bridge in Genoa. Procedia Struct. Integr. 2019, 18, 237–244. [Google Scholar] [CrossRef]
- Lin, W.; Taniguchi, N.; Yoda, T. Novel Method for Retrofitting Superstructures and Piers in Aged Steel Railway Bridges. J. Bridge Eng. 2017, 22, 05017009. [Google Scholar] [CrossRef]
- Dong, C.-Z.; Catbas, F.N. A Review of Computer Vision–Based Structural Health Monitoring at Local and Global Levels. Struct. Health Monit. 2021, 20, 692–743. [Google Scholar] [CrossRef]
- Zhang, C.; Mousavi, A.A.; Masri, S.F.; Gholipour, G.; Yan, K.; Li, X. Vibration Feature Extraction Using Signal Processing Techniques for Structural Health Monitoring: A Review. Mech. Syst. Signal Process. 2022, 177, 109175. [Google Scholar] [CrossRef]
- Abdulkarem, M.; Samsudin, K.; Rokhani, F.Z.; A Rasid, M.F. Wireless Sensor Network for Structural Health Monitoring: A Contemporary Review of Technologies, Challenges, and Future Direction. Struct. Health Monit. 2020, 19, 693–735. [Google Scholar] [CrossRef]
- Malekjafarian, A.; McGetrick, P.J.; OBrien, E.J. A Review of Indirect Bridge Monitoring Using Passing Vehicles. Shock Vib. 2015, 2015, 286139. [Google Scholar] [CrossRef] [Green Version]
- Malekjafarian, A.; Corbally, R.; Gong, W. A Review of Mobile Sensing of Bridges Using Moving Vehicles: Progress to Date, Challenges and Future Trends. Structures 2022, 44, 1466–1489. [Google Scholar] [CrossRef]
- Yang, Y.B.; Lin, C.W.; Yau, J.D. Extracting Bridge Frequencies from the Dynamic Response of a Passing Vehicle. J. Sound Vib. 2004, 272, 471–493. [Google Scholar] [CrossRef]
- Hester, D.; González, A. A Discussion on the Merits and Limitations of Using Drive-by Monitoring to Detect Localised Damage in a Bridge. Mech. Syst. Signal Process. 2017, 90, 234–253. [Google Scholar] [CrossRef] [Green Version]
- Lin, C.W.; Yang, Y.B. Use of a Passing Vehicle to Scan the Fundamental Bridge Frequencies: An Experimental Verification. Eng. Struct. 2005, 27, 1865–1878. [Google Scholar] [CrossRef]
- Miyamoto, A.; Yabe, A. Development of Practical Health Monitoring System for Short- and Medium-Span Bridges Based on Vibration Responses of City Bus. J. Civ. Struct. Health Monit. 2012, 2, 47–63. [Google Scholar] [CrossRef]
- Yang, Y.B.; Li, Y.C.; Chang, K.C. Constructing the Mode Shapes of a Bridge from a Passing Vehicle: A Theoretical Study. Smart Struct. Syst. 2014, 13, 797–819. [Google Scholar] [CrossRef]
- Malekjafarian, A.; OBrien, E.J. Identification of Bridge Mode Shapes Using Short Time Frequency Domain Decomposition of the Responses Measured in a Passing Vehicle. Eng. Struct. 2014, 81, 386–397. [Google Scholar] [CrossRef] [Green Version]
- Feng, K.; González, A.; Casero, M. A KNN Algorithm for Locating and Quantifying Stiffness Loss in a Bridge from the Forced Vibration Due to a Truck Crossing at Low Speed. Mech. Syst. Signal Process. 2021, 154, 107599. [Google Scholar] [CrossRef]
- Makki Alamdari, M.; Chang, K.C.; Kim, C.W.; Kildashti, K.; Kalhori, H. Transmissibility Performance Assessment for Drive-by Bridge Inspection. Eng. Struct. 2021, 242, 112485. [Google Scholar] [CrossRef]
- Lan, Y.; Lin, W.; Zhang, Y. Bridge Frequency Identification Using Vibration Responses from Sensors on a Passing Vehicle. In Proceedings of the Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability; CRC Press: Boca Raton, FL, USA, 2022. [Google Scholar]
- Lan, Y.; Lin, W.; Zhang, Y. Bridge Frequency Identification Using Multiple Sensor Responses of an Ordinary Vehicle. Int. J. Struct. Stab. Dyn. 2022, 23, 2350056. [Google Scholar] [CrossRef]
- Yang, Y.B.; Yang, J.P. State-of-the-Art Review on Modal Identification and Damage Detection of Bridges by Moving Test Vehicles. Int. J. Struct. Stab. Dyn. 2018, 18, 1850025. [Google Scholar] [CrossRef]
- Spiridonakos, M.D.; Chatzi, E.N.; Sudret, B. Polynomial Chaos Expansion Models for the Monitoring of Structures under Operational Variability. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2016, 2, B4016003. [Google Scholar] [CrossRef]
- Zhang, Y.; Miyamori, Y.; Mikami, S.; Saito, T. Vibration-Based Structural State Identification by a 1-Dimensional Convolutional Neural Network. Comput.-Aided Civ. Infrastruct. Eng. 2019, 34, 822–839. [Google Scholar] [CrossRef]
- de Almeida Cardoso, R.; Cury, A.; Barbosa, F. Automated Real-Time Damage Detection Strategy Using Raw Dynamic Measurements. Eng. Struct. 2019, 196, 109364. [Google Scholar] [CrossRef]
- Wang, Z.L.; Yang, J.P.; Shi, K.; Xu, H.; Qiu, F.Q.; Yang, Y.B. Recent Advances in Researches on Vehicle Scanning Method for Bridges. Int. J. Struct. Stab. Dyn. 2022, 22, 2230005. [Google Scholar] [CrossRef]
- OBrien, E.J.; McGetrick, P.J.; González, A. A Drive-by Inspection System via Vehicle Moving Force Identification. Smart Struct. Syst. 2014, 13, 821–848. [Google Scholar] [CrossRef] [Green Version]
- OBrien, E.J.; Keenahan, J. Drive-by Damage Detection in Bridges Using the Apparent Profile. Struct. Control Health Monit. 2015, 22, 813–825. [Google Scholar] [CrossRef] [Green Version]
- Lan, Y. Vertical Vehicle Displacement Based Drive-by Inspection of Bridge Damage with Parameter Optimization. J. Eng. Res. 2021, 9, 193–210. [Google Scholar] [CrossRef]
- Lan, Y. Improving the Drive-by Bridge Inspection Performance by Vehicle Parameter Optimization. In Proceedings of the 8th Asia Pacific Workshop on Structural Health Monitoring (8AMWSHM), Cairns, Queensland, Australia, 20 April 2021; pp. 195–202. [Google Scholar]
- Cerda, F.; Chen, S.; Bielak, J.; Garrett, J.H.; Rizzo, P.; Kovacevic, J. Indirect Structural Health Monitoring of a Simplified Laboratory-Scale Bridge Model. Smart Struct. Syst. 2014, 13, 849–868. [Google Scholar] [CrossRef]
- Liu, J.; Chen, S.; Bergés, M.; Bielak, J.; Garrett, J.H.; Kovačević, J.; Noh, H.Y. Diagnosis Algorithms for Indirect Structural Health Monitoring of a Bridge Model via Dimensionality Reduction. Mech. Syst. Signal Process. 2020, 136, 106454. [Google Scholar] [CrossRef]
- Sarwar, M.Z.; Cantero, D. Deep Autoencoder Architecture for Bridge Damage Assessment Using Responses from Several Vehicles. Eng. Struct. 2021, 246, 113064. [Google Scholar] [CrossRef]
- Liu, J.; Chen, B.; Chen, S.; Bergés, M.; Bielak, J.; Noh, H.Y. Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring. In Proceedings of the ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4–8 May 2020; pp. 3007–3011. [Google Scholar]
- Jantunen, E.; El-Thalji, I.; Baglee, D.; Lagö, T.L. Problems with using Fast Fourier Transform for rotating equipment: Is it time for an update? In Proceedings of the 27th International Congress of Condition Monitoring and Diagnostic Engineering, COMADEM 2014, Brisbane, Australia, 16–18 September 2014. [Google Scholar]
- Lan, Y.; Zhang, Y.; Lin, W. Diagnosis Algorithms for Indirect Bridge Health Monitoring via an Optimized AdaBoost-Linear SVM. Eng. Struct. 2023, 275, 115239. [Google Scholar] [CrossRef]
- Braverman, V. Sliding Window Algorithms. In Encyclopedia of Algorithms; Springer: New York, NY, USA, 2016; pp. 2006–2011. ISBN 978-1-4939-2864-4. [Google Scholar]
- Hou, J.; Li, Z.; Zhang, Q.; Jankowski, Ł.; Zhang, H. Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models. Int. J. Struct. Stab. Dyn. 2020, 20, 2050124. [Google Scholar] [CrossRef]
- Xu, H.; Liu, Y.H.; Wang, Z.L.; Shi, K.; Zhang, B.; Yang, Y.B. General Contact Response of Single-Axle Two-Mass Test Vehicles for Scanning Bridge Frequencies Considering Suspension Effect. Eng. Struct. 2022, 270, 114880. [Google Scholar] [CrossRef]
- Ketkar, N. Deep Learning with Python; Apress: Berkeley, CA, USA, 2017; ISBN 978-1-4842-2765-7. [Google Scholar]
- Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA, 2016; ISBN 978-0-262-03561-3. [Google Scholar]
- Thorsen, A.; Lederman, G.; Oshima, Y.; Bielak, J.; Noh, H.Y. Mitigating the Effects of Variable Speed on Drive-by Infrastructure Monitoring. In Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, Denver, DE, USA, 27 March 2015; Volume 9435, pp. 75–83. [Google Scholar]
- Mei, Q.; Gül, M.; Boay, M. Indirect Health Monitoring of Bridges Using Mel-Frequency Cepstral Coefficients and Principal Component Analysis. Mech. Syst. Signal Process. 2019, 119, 523–546. [Google Scholar] [CrossRef]
- Li, Z.; Lin, W.; Zhang, Y. Real-Time Drive-by Bridge Damage Detection Using Deep Auto-Encoder. Structures 2023, 47, 1167–1181. [Google Scholar] [CrossRef]
- Evgeniou, T.; Pontil, M. Support Vector Machines: Theory and Applications; Springer: Berlin/Heidelberg, Germany, 2001; pp. 249–257. ISBN 978-3-540-44673-6. [Google Scholar]
- Chang, C.-C.; Lin, C.-J. LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 27. [Google Scholar] [CrossRef]
- Rasmussen, C.E.; Williams, C.K.I. Gaussian Processes for Machine Learning; MIT Press: Cambridge, MA, USA, 2005. [Google Scholar]
- Ho, T.K. Random Decision Forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 14–16 August 1995; Volume 1, pp. 278–282. [Google Scholar]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- LaValle, S.M.; Branicky, M.S.; Lindemann, S.R. On the Relationship between Classical Grid Search and Probabilistic Roadmaps. Int. J. Robot. Res. 2004, 23, 673–692. [Google Scholar] [CrossRef]
- Kim, C.W.; Isemoto, R.; McGetrick, P.J.; Kawatani, M.; OBrien, E.J. Drive-by Bridge Inspection from Three Different Approaches. Smart Struct. Syst. 2014, 13, 775–796. [Google Scholar] [CrossRef] [Green Version]
- Type 4371 Piezoelectric Charge Accelerometer|Brüel & Kjær. Available online: https://www.bksv.com/en/transducers/vibration/accelerometers/charge/4371 (accessed on 2 October 2022).
- Li, Z.; Lan, Y.; Lin, W. Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles. Materials 2023, 16, 1872. [Google Scholar] [CrossRef] [PubMed]
- Hastie, T.; Tibshirani, R.; Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer Series in Statistics; Springer: New York, NY, USA, 2009; ISBN 978-0-387-84857-0. [Google Scholar]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat Methods 2020, 17, 261–272. [Google Scholar] [CrossRef] [Green Version]
Case No. | Location | Weight | Runs | Case No. | Location | Weight | Runs |
---|---|---|---|---|---|---|---|
0 | 0 | 0 (Healthy) | 200 | 2 | 2 m | 20 kg (4%) | 200 |
1 | 0.4 m | 20 kg (4%) | 200 | 3 | 2.8 m | 20 kg (4%) | 200 |
Algorithm | Configuration | Algorithm | Configuration |
---|---|---|---|
Linear-SVM | C = 2 | ANN | hidden_layer_sizes = (12, 5), alpha = 1, max_iter = 800 |
RBF-SVM | Gamma = 0.01, C = 2 | RF | n_estimators = 900, max_features = 25 |
GP | Kernel = 100 × RBF (100) |
Case No. | Type | Linear-SVM | RBF-SVM | GP | ANN | RF |
---|---|---|---|---|---|---|
Case1 | Original | 78.3% (0.62 s) | 71.7% (0.61 s) | 75.0% (6.48 s) | 78.3% (28.11 s) | 76.7% (4.26 s) |
Processed | 90.0% (0.02 s) | 83.3% (0.02 s) | 88.3% (2.66 s) | 91.7% (2.44 s) | 90.0% (2.72 s) | |
Case2 | Original | 80.0% (0.61 s) | 75.0% (0.59 s) | 76.7% (6.25 s) | 78.3% (27.42 s) | 78.3% (4.21 s) |
Processed | 93.3% (0.02 s) | 86.7% (0.02 s) | 88.3% (2.75 s) | 93.3% (2.45 s) | 91.7% (2.79 s) | |
Case3 | Original | 80.0% (0.59 s) | 75.0% (0.59 s) | 76.7% (6.63 s) | 80.0% (27.89 s) | 76.7% (4.15 s) |
Processed | 95.0% (0.02 s) | 88.3% (0.02 s) | 91.7% (2.42 s) | 96.7% (2.30 s) | 95.0% (2.61 s) | |
Avg. imp. | 13.3% (0.59 s) | 12.2% (0.58 s) | 13.3% (3.84 s) | 15.0% (25.4 s) | 15.0% (1.5 s) |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lan, Y.; Li, Z.; Lin, W. A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study. Materials 2023, 16, 2624. https://doi.org/10.3390/ma16072624
Lan Y, Li Z, Lin W. A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study. Materials. 2023; 16(7):2624. https://doi.org/10.3390/ma16072624
Chicago/Turabian StyleLan, Yifu, Zhenkun Li, and Weiwei Lin. 2023. "A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study" Materials 16, no. 7: 2624. https://doi.org/10.3390/ma16072624