ADVANCEMENTS IN STRUCTURAL HEALTH MONITORING: A REVIEW OF MACHINE LEARNING APPROACHES FOR DAMAGE DETECTION AND ASSESSMENT
Main Article Content
Abstract
Structural Health Monitoring (SHM) is a crucial discipline geared towards detecting damage in engineering structures early, aiming to prevent failures and facilitate condition-based maintenance. Traditional SHM methodologies, relying on visual inspections, analytical models, and signal processing, exhibit inherent limitations. The advent of machine learning has introduced data-driven solutions to automate various aspects of SHM, including damage detection, localization, classification, and prognosis.
This paper provides a comprehensive review of recent studies exploring supervised, unsupervised, and deep learning techniques in vibration-based, image-based, and multi-sensor SHM. Support vector machines, neural networks, deep convolutional neural networks, and other advanced algorithms have demonstrated exceptional performance in assessing damage using real-world structural datasets.
Despite these successes, practical challenges persist, particularly in addressing variability and deploying machine learning models effectively on full-scale structures. Overcoming these challenges necessitates a more integrated, cross-disciplinary approach, merging mechanical engineering fundamentals with machine learning expertise. This synergy can pave the way for robust field implementation and further enhance the reliability of SHM systems.
The transformative potential of machine learning in SHM cannot be understated. Beyond merely shifting from time-based maintenance to condition-based strategies, machine learning can automate and continuously evaluate structural integrity, ensuring the longevity of engineering structures. As we delve deeper into the intersection of mechanical engineering and machine learning, the prospect of a future where SHM seamlessly integrates with advanced technologies becomes increasingly tangible.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Metaxa, S., Kalkanis, K., Psomopoulos, C. S., Kaminaris, S. D., & Ioannidis, G. (2019). A review of structural health monitoring methods for composite materials. Procedia Structural Integrity, 22, 369–375. DOI: https://doi.org/10.1016/j.prostr.2020.01.046
Pereira, A. S. A. (2021). Understanding and exploring virtual sensing and its capabilities for structural health monitoring.
Agdas, D., Rice, J. A., Martinez, J. R., & Lasa, I. R. (2016). Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods. Journal of Performance of Constructed Facilities, 30(3), 04015049. DOI: https://doi.org/10.1061/(ASCE)CF.1943-5509.0000802
Harley, J. B., & Sparkman, D. (2019). Machine learning and NDE: Past, present, and future. 2102. AIP Publishing. DOI: https://doi.org/10.1063/1.5099819
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170.
Worden, K., Manson, G., & Fieller, N. R. J. (2000). Damage detection using outlier analysis. Journal of Sound and Vibration, 229(3), 647–667. DOI: https://doi.org/10.1006/jsvi.1999.2514
Ye, X. W., Jin, T., & Yun, C. B. (2019). A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct. Syst, 24(5), 567–585.
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2021). A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical Systems and Signal Processing, 147, 107077.
Modarres, C., Astorga, N., Droguett, E. L., & Meruane, V. (2018). Convolutional neural networks for automated damage recognition and damage type identification. Structural Control and Health Monitoring, 25(10), e2230 DOI: https://doi.org/10.1002/stc.2230
Aria, A., Lopez Droguett, E., Azarm, S., & Modarres, M. (2020). Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion. Structural Health Monitoring, 19(5), 1542–1559. DOI: https://doi.org/10.1177/1475921719890616
Belguesmi, L., Hajji, M., Mansouri, M., Harkat, M.-F., Kouadri, A., Nounou, H., & Nounou, M. (2020). Machine learning approaches for fault detection and diagnosis of induction motors. 692–698. IEEE. DOI: https://doi.org/10.1109/SSD49366.2020.9364240
Kumar, P., & Hati, A. S. (2021). Review on machine learning algorithm based fault detection in induction motors. Archives of Computational Methods in Engineering, 28, 1929–1940. DOI: https://doi.org/10.1007/s11831-020-09446-w
Hoskere, V., Narazaki, Y., Hoang, T. A., & Spencer, B. F., Jr. (2020). MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure. Journal of Civil Structural Health Monitoring, 10, 757–773.
Kang, M. (2018). Machine learning: Anomaly detection. Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 131–162. DOI: https://doi.org/10.1002/9781119515326.ch6
Chen, M., Li, Z., Lei, X., Liang, S., Zhao, S., & Su, Y. (2023). Unsupervised Fault Detection Driven by Multivariate Time Series for Aeroengines. Journal of Aerospace Engineering, 36(2), 04022129. DOI: https://doi.org/10.1061/JAEEEZ.ASENG-4576
Chen, X., Chen, Z., Hu, S., Gu, C., Guo, J., & Qin, X. (2023). A feature decomposition-based deep transfer learning framework for concrete dam deformation prediction with observational insufficiency. Advanced Engineering Informatics, 58, 102175. DOI: https://doi.org/10.1016/j.aei.2023.102175
Soleimani-Babakamali, M. H. (2022). Toward a general novelty detection framework in structural health monitoring; challenges and opportunities in deep learning. Virginia Tech. DOI: https://doi.org/10.1111/mice.12812
Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 303–315. DOI: https://doi.org/10.1098/rsta.2006.1928
Lee, J. J., & Yun, C. B. (2006). Damage diagnosis of steel girder bridges using ambient vibration data. Engineering Structures, 28(6), 912–925. DOI: https://doi.org/10.1016/j.engstruct.2005.10.017
Shu, J., Zhang, C., Gao, Y., & Niu, Y. (2023). A multi-task learning-based automatic blind identification procedure for operational modal analysis. Mechanical Systems and Signal Processing, 187, 109959. DOI: https://doi.org/10.1016/j.ymssp.2022.109959
Teng, Z., Teng, S., Zhang, J., Chen, G., & Cui, F. (2020). Structural damage detection based on real-time vibration signal and convolutional neural network. Applied Sciences, 10(14), 4720. DOI: https://doi.org/10.3390/app10144720
Cha, Y.-J., & Wang, Z. (2018). Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm. Structural Health Monitoring, 17(2), 313–324. DOI: https://doi.org/10.1177/1475921717691260
Rizvi, S. H. M., Abbas, M., & Tayyab, S. M. T. (2023). Anomaly Detection and Localization Using LSTM Based Autoencoder with Maximal Overlap Discrete Wavelet Transform for Structural Health Monitoring. DOI: https://doi.org/10.20944/preprints202306.1007.v1
Chandrasekhar, K., Stevanovic, N., Cross, E. J., Dervilis, N., & Worden, K. (2021). Damage detection in operational wind turbine blades using a new approach based on machine learning. Renewable Energy, 168, 1249–1264. DOI: https://doi.org/10.1016/j.renene.2020.12.119
Dong, C.-Z., & Catbas, F. N. (2021). A review of computer vision–based structural health monitoring at local and global levels. Structural Health Monitoring, 20(2), 692–743.
Azimi, M., Eslamlou, A. D., & Pekcan, G. (2020). Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors, 20(10), 2778.
Li, H.-N., Yi, T.-H., Ren, L., Li, D.-S., & Huo, L.-S. (2014). Reviews on innovations and applications in structural health monitoring for infrastructures. Structural Monitoring and Maintenance, 1(1), 1. DOI: https://doi.org/10.12989/smm.2014.1.1.001
Worden, K., & Manson, G. (2007). The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 515–537. DOI: https://doi.org/10.1098/rsta.2006.1938
Wang, N., Zhao, X., Zhao, P., Zhang, Y., Zou, Z., & Ou, J. (2019). Automatic damage detection of historic masonry buildings based on mobile deep learning. Automation in Construction, 103, 53–66. DOI: https://doi.org/10.1016/j.autcon.2019.03.003
Kim, J.-T., & Stubbs, N. (1995). Model-uncertainty impact and damage-detection accuracy in plate girder. Journal of Structural Engineering, 121(10), 1409–1417. DOI: https://doi.org/10.1061/(ASCE)0733-9445(1995)121:10(1409)
Neves, A. C., Gonzalez, I., Leander, J., & Karoumi, R. (2017). Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. Journal of Civil Structural Health Monitoring, 7, 689–702.
Santos, A., Figueiredo, E., Silva, M. F. M., Sales, C. S., & Costa, J. (2016). Machine learning algorithms for damage detection: Kernel-based approaches. Journal of Sound and Vibration, 363, 584–599. DOI: https://doi.org/10.1016/j.jsv.2015.11.008
Srinivas, V., Sasmal, S., & Ramanjaneyulu, K. (2014). Damage-sensitive features from non-linear vibration response of reinforced concrete structures. Structural Health Monitoring, 13(3), 233–250. DOI: https://doi.org/10.1177/1475921713520028
Flah, M., Nunez, I., Ben Chaabene, W., & Nehdi, M. L. (2021). Machine learning algorithms in civil structural health monitoring: A systematic review. Archives of Computational Methods in Engineering, 28, 2621–2643. DOI: https://doi.org/10.1007/s11831-020-09471-9
Yang, K., Ding, Y., Jiang, H., Zhao, H., & Luo, G. (2022). A two‐stage data cleansing method for bridge global positioning system monitoring data based on bi‐direction long and short term memory anomaly identification and conditional generative adversarial networks data repair. Structural Control and Health Monitoring, 29(9), e2993. DOI: https://doi.org/10.1002/stc.2993
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170.
Kiranyaz, S., Ince, T., Hamila, R., & Gabbouj, M. (2015). Convolutional neural networks for patient-specific ECG classification. 2608–2611. IEEE. DOI: https://doi.org/10.1109/EMBC.2015.7318926
An, Y., Chatzi, E., Sim, S., Laflamme, S., Blachowski, B., & Ou, J. (2019). Recent progress and future trends on damage identification methods for bridge structures. Structural Control and Health Monitoring, 26(10), e2416. DOI: https://doi.org/10.1002/stc.2416
Lomazzi, L., Giglio, M., & Cadini, F. (2023). Towards a deep learning-based unified approach for structural damage detection, localisation and quantification. Engineering Applications of Artificial Intelligence, 121, 106003. DOI: https://doi.org/10.1016/j.engappai.2023.106003
Yeum, C. M., & Dyke, S. J. (2015). Vision‐based automated crack detection for bridge inspection. Computer‐Aided Civil and Infrastructure Engineering, 30(10), 759–770. DOI: https://doi.org/10.1111/mice.12141
Bhatt, P. M., Malhan, R. K., Rajendran, P., Shah, B. C., Thakar, S., Yoon, Y. J., & Gupta, S. K. (2021). Image-based surface defect detection using deep learning: A review. Journal of Computing and Information Science in Engineering, 21(4), 040801. DOI: https://doi.org/10.1115/1.4049535
Rizvi, S. H. M., & Abbas, M. (2023). From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods. Engineering Research Express, 5(3), 032003. DOI: https://doi.org/10.1088/2631-8695/acefae
Yessoufou, F., & Zhu, J. (2023). Classification and regression-based convolutional neural network and long short-term memory configuration for bridge damage identification using long-term monitoring vibration data. Structural Health Monitoring, 14759217231161811. DOI: https://doi.org/10.1177/14759217231161811
Liu, B., Gan, H., Chen, D., & Shu, Z. (2022). Research on Fault Early Warning of Marine Diesel Engine Based on CNN-BiGRU. Journal of Marine Science and Engineering, 11(1), 56. DOI: https://doi.org/10.3390/jmse11010056
Bao, Y., Tang, Z., Li, H., & Zhang, Y. (2019). Computer vision and deep learning–based data anomaly detection method for structural health monitoring. Structural Health Monitoring, 18(2), 401–421. DOI: https://doi.org/10.1177/1475921718757405
Figueiredo, E., Figueiras, J., Park, G., Farrar, C. R., & Worden, K. (2011). Influence of the autoregressive model order on damage detection. Computer‐Aided Civil and Infrastructure Engineering, 26(3), 225–238. DOI: https://doi.org/10.1111/j.1467-8667.2010.00685.x
Neves, A. C., Gonzalez, I., Leander, J., & Karoumi, R. (2017). Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. Journal of Civil Structural Health Monitoring, 7, 689–702. DOI: https://doi.org/10.1007/s13349-017-0252-5
Ye, X.-W., Jin, T., & Chen, P.-Y. (2019). Structural crack detection using deep learning–based fully convolutional networks. Advances in Structural Engineering, 22(16), 3412–3419.
Siow, P. Y., Ong, Z. C., Khoo, S. Y., & Lim, K.-S. (2023). Hybrid modal-machine learning damage identification approach for beam-like structures. Journal of Vibration and Control, 10775463231209008. DOI: https://doi.org/10.1177/10775463231209008
Zhao, B., Cheng, C., Peng, Z., Dong, X., & Meng, G. (2020). Detecting the early damages in structures with nonlinear output frequency response functions and the CNN-LSTM model. IEEE Transactions on Instrumentation and Measurement, 69(12), 9557–9567. DOI: https://doi.org/10.1109/TIM.2020.3005113
Teng, S., Chen, G., Yan, Z., Cheng, L., & Bassir, D. (2023). Vibration-based structural damage detection using 1-D convolutional neural network and transfer learning. Structural Health Monitoring, 22(4), 2888–2909. DOI: https://doi.org/10.1177/14759217221137931
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170. DOI: https://doi.org/10.1016/j.jsv.2016.10.043
Li, Y., Xu, M., Wei, Y., & Huang, W. (2016). A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree. Measurement, 77, 80–94. DOI: https://doi.org/10.1016/j.measurement.2015.08.034
Wang, Y., Xu, C., Wang, Y., & Cheng, X. (2021). A comprehensive diagnosis method of rolling bearing fault based on CEEMDAN-DFA-improved wavelet threshold function and QPSO-MPE-SVM. Entropy, 23(9), 1142. DOI: https://doi.org/10.3390/e23091142
Gao, Y., & Mosalam, K. M. (2018). Deep transfer learning for image‐based structural damage recognition. Computer‐Aided Civil and Infrastructure Engineering, 33(9), 748–768. DOI: https://doi.org/10.1111/mice.12363
Lee, K., Byun, N., & Shin, D. H. (2020). A damage localization approach for rahmen bridge based on convolutional neural network. KSCE Journal of Civil Engineering, 24(1), 1–9. DOI: https://doi.org/10.1007/s12205-020-0707-9
Won, J., Park, J.-W., Jang, S., Jin, K., & Kim, Y. (2021). Automated structural damage identification using data normalization and 1-dimensional convolutional neural network. Applied Sciences, 11(6), 2610. DOI: https://doi.org/10.3390/app11062610
Hoskere, V., Narazaki, Y., Hoang, T. A., & Spencer, B. F., Jr. (2020). MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure. Journal of Civil Structural Health Monitoring, 10, 757–773. DOI: https://doi.org/10.1007/s13349-020-00409-0
Zhuang, L., Luo, K., & Yang, Z. (2024). A multimodal gated recurrent unit neural network model for damage assessment in CFRP composites based on Lamb waves and minimal sensing. IEEE Transactions on Instrumentation and Measurement. DOI: https://doi.org/10.1109/TIM.2023.3348884
Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018). Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types. Computer‐Aided Civil and Infrastructure Engineering, 33(9), 731–747.
Ye, X.-W., Jin, T., & Chen, P.-Y. (2019). Structural crack detection using deep learning–based fully convolutional networks. Advances in Structural Engineering, 22(16), 3412–3419. DOI: https://doi.org/10.1177/1369433219836292
Fan, W., & Qiao, P. (2011). Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring, 10(1), 83–111. DOI: https://doi.org/10.1177/1475921710365419
Jang, S., Jo, H., Cho, S., Mechitov, K., Rice, J. A., Sim, S.-H., … Agha, G. (2010). Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation. Smart Structures and Systems, 6(5_6), 439–459. DOI: https://doi.org/10.12989/sss.2010.6.5_6.439
Cho, S., Jo, H., Jang, S., Park, J., Jung, H.-J., Yun, C.-B., … Seo, J.-W. (2010). Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses. Smart Structures and Systems, 6(5–6), 461–480. DOI: https://doi.org/10.12989/sss.2010.6.5_6.461
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2021). A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical Systems and Signal Processing, 147, 107077. DOI: https://doi.org/10.1016/j.ymssp.2020.107077
Azimi, M., Eslamlou, A. D., & Pekcan, G. (2020). Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors, 20(10), 2778. DOI: https://doi.org/10.3390/s20102778
Mousavi, Z., Varahram, S., Ettefagh, M. M., Sadeghi, M. H., & Razavi, S. N. (2021). Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure. Structural Health Monitoring, 20(1), 379–405. DOI: https://doi.org/10.1177/1475921720932614
He, Y., Huang, Z., Liu, D., Zhang, L., & Liu, Y. (2022). A Novel Structural Damage Identification Method Using a Hybrid Deep Learning Framework. Buildings, 12(12), 2130. DOI: https://doi.org/10.3390/buildings12122130
Tabatabaeian, A., Jerkovic, B., Harrison, P., Marchiori, E., & Fotouhi, M. (2023). Barely visible impact damage detection in composite structures using deep learning networks with varying complexities. Composites Part B: Engineering, 264, 110907. DOI: https://doi.org/10.1016/j.compositesb.2023.110907
Jiang, G., He, H., Yan, J., & Xie, P. (2018). Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Transactions on Industrial Electronics, 66(4), 3196–3207. DOI: https://doi.org/10.1109/TIE.2018.2844805
Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering & System Safety, 182, 208–218. DOI: https://doi.org/10.1016/j.ress.2018.11.011
Liu, B., Gao, Z., Lu, B., Dong, H., & An, Z. (2022). Deep Learning-Based Remaining Useful Life Estimation of Bearings with Time-Frequency Information. Sensors, 22(19), 7402. DOI: https://doi.org/10.3390/s22197402
Cao, S., Ouyang, H., & Cheng, L. (2019). Adaptive damage localization based on locally perturbed dynamic equilibrium and hierarchical clustering. Smart Materials and Structures, 28(7), 075003. DOI: https://doi.org/10.1088/1361-665X/ab1abe
Xing, C., Ma, L., & Yang, X. (2016). Stacked denoise autoencoder based feature extraction and classification for hyperspectral images. Journal of Sensors, 2016. DOI: https://doi.org/10.1155/2016/3632943
Lee, J., Lee, K.-C., Cho, S., & Sim, S.-H. (2017). Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges. Sensors, 17(10), 2317. DOI: https://doi.org/10.3390/s17102317
Pâques, M., Law‐Hine, D., Hamedane, O. A., Magnaval, G., & Allezard, N. (2023). Automatic Multi‐label Classification of Bridge Components and Defects Based on Inspection Photographs. Ce/Papers, 6(5), 1080–1086. DOI: https://doi.org/10.1002/cepa.2072
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98–109. DOI: https://doi.org/10.1016/j.neucom.2017.02.045
Shirazi, M. I., Khatir, S., Benaissa, B., Mirjalili, S., & Wahab, M. A. (2023). Damage assessment in laminated composite plates using modal Strain Energy and YUKI-ANN algorithm. Composite Structures, 303, 116272. DOI: https://doi.org/10.1016/j.compstruct.2022.116272
Dong, C.-Z., & Catbas, F. N. (2021). A review of computer vision–based structural health monitoring at local and global levels. Structural Health Monitoring, 20(2), 692–743. DOI: https://doi.org/10.1177/1475921720935585
Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018). Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types. Computer‐Aided Civil and Infrastructure Engineering, 33(9), 731–747. DOI: https://doi.org/10.1111/mice.12334
Gopalakrishnan, K., Gholami, H., Vidyadharan, A., Choudhary, A., & Agrawal, A. (2018). Crack damage detection in unmanned aerial vehicle images of civil infrastructure using pre-trained deep learning model. Int. J. Traffic Transp. Eng, 8(1), 1–14. DOI: https://doi.org/10.7708/ijtte.2018.8(1).01