. School of Transportation Science and Engineering, Beihang University, Beijing 100191, China . CCSE Lab, Beihang University, Beijing 100191, China . State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics Chinese Academy of Sciences, Wuhan 430071, China . University of Chinese Academy of Sciences, Beijing 100049, China
Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters, so monitoring is required. Data collected by structural health monitoring (SHM) systems are easily affected by many factors, such as temperature, sensor fluctuation, sensor failure, which can introduce a lot of noise, increasing the difficulty of structural anomaly identification. To address this problem, this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder (CIDAE), a denoising autoencoder-based deep learning model for SHM of civil infrastructure. As a case study, the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation. Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted. It is concluded that CIDAE is superior to traditional methods.
. [J]. Frontiers of Structural and Civil Engineering, 2024, 18(10): 1479-1491.
Junchen YE, Zhixin ZHANG, Ke CHENG, Xuyan TAN, Bowen DU, Weizhong CHEN. Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method. Front. Struct. Civ. Eng., 2024, 18(10): 1479-1491.
M F Bado, J R Casas. A review of recent distributed optical fiber sensors applications for civil engineering structural health monitoring. Sensors, 2021, 21(5): 1818–1901 https://doi.org/10.3390/s21051818
2
Y Bao, H Li. Machine learning paradigm for structural health monitoring. Structural Health Monitoring, 2021, 20(4): 1353–1372 https://doi.org/10.1177/1475921720972416
3
J Chen, X Jiang, Y Yan, Q Lang, H Wang, Q Ai. Dynamic warning method for structural health monitoring data based on arima: Case study of Hong Kong–Zhuhai–Macao bridge immersed tunnel. Sensors, 2022, 22(16): 6185–6202 https://doi.org/10.3390/s22166185
4
R Hou, Y Xia. Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019. Journal of Sound and Vibration, 2021, 491: 115741 https://doi.org/10.1016/j.jsv.2020.115741
5
H P Chen. Structural Health Monitoring of Large Civil Engineering Structures. CSU Academic Report, 2018
6
A Entezami, H Sarmadi, B Saeedi Razavi. An innovative hybrid strategy for structural health monitoring by modal flexibility and clustering methods. Journal of Civil Structural Health Monitoring, 2020, 10(5): 845–859 https://doi.org/10.1007/s13349-020-00421-4
7
P Cao, S Qi, J Tang. Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis. Smart Materials and Structures, 2018, 27(3): 035020 https://doi.org/10.1088/1361-665X/aaacba
8
E Z Moore, J M Nichols, K D Murphy. Model-based SHM: Demonstration of identification of a crack in a thin plate using free vibration data. Mechanical Systems and Signal Processing, 2012, 29: 284–295 https://doi.org/10.1016/j.ymssp.2011.09.022
9
L Xu, K Wang, X Yang, Y Su, J Yang, Y Liao, P Zhou, Z Su. Model-driven fatigue crack characterization and growth prediction: A two-step, 3-d fatigue damage modeling framework for structural health monitoring. International Journal of Mechanical Sciences, 2021, 195: 106226 https://doi.org/10.1016/j.ijmecsci.2020.106226
10
A Malekloo, E Ozer, M AlHamaydeh, M Girolami. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 2022, 21(4): 1906–1955 https://doi.org/10.1177/14759217211036880
11
A A Mosavi, D Dickey, R Seracino, S Rizkalla. Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mechanical Systems and Signal Processing, 2012, 26: 254–267 https://doi.org/10.1016/j.ymssp.2011.06.009
12
S Xiao, S Li. LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes. Frontiers of Structural and Civil Engineering, 2022, 16(7): 871–881 https://doi.org/10.1007/s11709-022-0863-8
13
D TibaduizaM A Torres-ArredondoJ VitolaM AnayaF Pozo. A damage classification approach for structural health monitoring using machine learning. Complexity, 2018, 5081283
14
Z Sheikh Khozani, K Khosravi, M Torabi, A Mosavi, B Rezaei, T Rabczuk. Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models. Frontiers of Structural and Civil Engineering, 2020, 14(5): 1097–1109 https://doi.org/10.1007/s11709-020-0634-3
15
H V T Mai, M H Nguyen, S H Trinh, H B Ly. Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete. Frontiers of Structural and Civil Engineering, 2023, 17(2): 284–305 https://doi.org/10.1007/s11709-022-0901-6
16
Z WangY J Cha. Unsupervised machine and deep learning methods for structural damage detection: A comparative study. Engineering Reports, 2022, e12551
17
X W Ye, T Jin, C B Yun. A review on deep learning-based structural health monitoring of civil infrastructures. Smart Structures and Systems, 2019, 24(5): 567–585
18
A JansenK Geißler. Multi-feature anomaly detection for structural health monitoring of a road bridge using an autoencoder. In: 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure–SHMII. 2021, 10
19
A MoallemiA BurrelloD BrunelliL Benini. Model-based vs. data-driven approaches for anomaly detection in structural health monitoring: A case study. In: Proceedings of 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). New York: IEEE, 2021
20
X Shu, T Bao, Y Zhou, R Xu, Y Li, K Zhang. Unsupervised dam anomaly detection with spatial-temporal variational autoencoder. Structural Health Monitoring, 2023, 22(1): 39–55
21
Y J Cha, W Choi, O Büyüköztürk. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361–378 https://doi.org/10.1111/mice.12263
22
B Chiaia, G Marasco, S Aiello. Deep convolutional neural network for multi-level non-invasive tunnel lining assessment. Frontiers of Structural and Civil Engineering, 2022, 16(2): 214–223 https://doi.org/10.1007/s11709-021-0800-2
23
Y Bao, Z Tang, H Li, Y Zhang. Computer vision and deep learning-based data anomaly detection method for structural health monitoring. Structural Health Monitoring, 2019, 18(2): 401–421 https://doi.org/10.1177/1475921718757405
24
H B Huang, T H Yi, H N Li. Anomaly identification of structural health monitoring data using dynamic independent component analysis. Journal of Computing in Civil Engineering, 2020, 34(5): 04020025 https://doi.org/10.1061/(ASCE)CP.1943-5487.0000905
25
Z Wang, Y J Cha. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Structural Health Monitoring, 2021, 20(1): 406–425 https://doi.org/10.1177/1475921720934051
26
Y J Cha, Z Wang. Unsupervised novelty detection-based structural damage localization using a density peaks-based fast clustering algorithm. Structural Health Monitoring, 2018, 17(2): 313–324 https://doi.org/10.1177/1475921717691260
27
E Favarelli, E Testi, A Giorgetti. The impact of sensing parameters on data management and anomaly detection in structural health monitoring. Journal of Civil Structural Health Monitoring, 2022, 12(6): 1–13 https://doi.org/10.1007/s13349-022-00566-4
28
S Yan, H Shao, Y Xiao, B Liu, J Wan. Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises. Robotics and Computer-integrated Manufacturing, 2023, 79: 102441 https://doi.org/10.1016/j.rcim.2022.102441
29
A Mostafavi, Y J Cha. Deep learning-based active noise control on construction sites. Automation in Construction, 2023, 151: 104885 https://doi.org/10.1016/j.autcon.2023.104885
30
Y J Cha, A Mostafavi, S S Benipal. Benipal. Dnoisenet: Deep learning-based feedback active noise control in various noisy environments. Engineering Applications of Artificial Intelligence, 2023, 121: 105971 https://doi.org/10.1016/j.engappai.2023.105971
31
A S Krasichkov, E B Grigoriev, M I Bogachev, E M Nifontov. Shape anomaly detection under strong measurement noise: An analytical approach to adaptive thresholding. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 2015, 92(4): 042927 https://doi.org/10.1103/PhysRevE.92.042927
32
M Raginsky, R M Willett, C Horn, J Silva, R F Marcia. Sequential anomaly detection in the presence of noise and limited feedback. IEEE Transactions on Information Theory, 2012, 58(8): 5544–5562 https://doi.org/10.1109/TIT.2012.2201375
33
P VincentH LarochelleY BengioP A Manzagol. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning. New York: ACM, 2008, 1096–1103
34
X Tan, W Chen, X Tan, T Zou, B Du. Prediction for the future mechanical behavior of underwater shield tunnel fusing deep learning algorithm on shm data. Tunnelling and Underground Space Technology, 2022, 125: 104504 https://doi.org/10.1016/j.tust.2022.104504
35
B ZhouS LiuB HooiX ChengJ Ye. Beatgan: Anomalous rhythm detection using adversarially generated time series. In: Proceedings of IJCAI-19. San Mateo: IJCAI, 2019, 4433–4439
36
H Hotelling. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 1933, 24(6): 417–441 https://doi.org/10.1037/h0071325
37
D Rumelhart, G E Hinton, R J Williams. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533–536
