1. College of Civil Engineering, Tongji University, Shanghai 200092, China 2. China Construction Eighth Engineering Division Co., Ltd., Shanghai 200135, China
Single-layer reticulated shells (SLRSs) find widespread application in the roofs of crucial public structures, such as gymnasiums and exhibition center. In this paper, a new neural-network-based method for structural damage identification in SLRSs is proposed. First, a damage vector index, NDL, that is related only to the damage localization, is proposed for SLRSs, and a damage data set is constructed from NDL data. On the basis of visualization of the NDL damage data set, the structural damaged region locations are identified using convolutional neural networks (CNNs). By cross-dividing the damaged region locations and using parallel CNNs for each regional location, the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated. Second, a damage vector index, DS, that is related to the damage location and damage degree, is proposed for SLRSs. Based on the damaged region identified previously, a fully connected neural network (FCNN) is constructed to identify the location and damage degree of members. The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS. The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.
E P Carden, P Fanning. Vibration based condition monitoring: A review. Structural Health Monitoring, 2004, 3(4): 355–377 https://doi.org/10.1177/1475921704047500
2
Y Y Li, L Cheng, L H Yam, W O Wong. Identification of damage locations for plate-like structures using damage sensitive indices: Strain modal approach. Computers & Structures, 2002, 80(25): 1881–1894 https://doi.org/10.1016/S0045-7949(02)00209-2
3
Q Lu, G Ren, Y Zhao. Multiple damage location with flexibility curvature and relative frequency change for beam structures. Journal of Sound and Vibration, 2002, 253(5): 1101–1114 https://doi.org/10.1006/jsvi.2001.4092
4
Q W Yang, B X Sun. Structural damage localization and quantification using static test data. Structural Health Monitoring, 2011, 10(4): 381–389 https://doi.org/10.1177/1475921710379517
5
T T Truong, D Dinh-Cong, J Lee, T Nguyen-Thoi. An effective deep feedforward neural networks (DFNN) method for damage identification of truss structures using noisy incomplete modal data. Journal of Building Engineering, 2020, 30: 101244 https://doi.org/10.1016/j.jobe.2020.101244
6
M Mishra, P B Lourenco, G V Ramana. Structural health monitoring of civil engineering structures by using the internet of things: A review. Journal of Building Engineering, 2022, 48: 103954 https://doi.org/10.1016/j.jobe.2021.103954
7
W Fan, P Z Qiao. Vibration-based damage identification methods: A review and comparative study. Structural Health Monitoring, 2011, 10(1): 83–111 https://doi.org/10.1177/1475921710365419
8
E Reynders, J Houbrechts, G de Roeck. Fully automated (operational) modal analysis. Mechanical Systems and Signal Processing, 2012, 29: 228–250 https://doi.org/10.1016/j.ymssp.2012.01.007
9
R 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
10
F Magalhães, A Cunha, E Caetano. Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection. Mechanical Systems and Signal Processing, 2012, 28: 212–228 https://doi.org/10.1016/j.ymssp.2011.06.011
11
C BollerF K ChangY Fujinoeds. Encyclopedia of Structural Health Monitoring. Hoboken, NJ: John Wiley and Sons, 2009
12
A Deraemaeker, E Reynders, G de Roeck, J Kullaa. Vibration-based structural health monitoring using output-only measurements under changing environment. Mechanical Systems and Signal Processing, 2008, 22(1): 34–56 https://doi.org/10.1016/j.ymssp.2007.07.004
13
H Guo, X Zhuang, T Rabczuk. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456 https://doi.org/10.32604/cmc.2019.06660
14
C Anitescu, E Atroshchenko, N Alajlan, T Rabczuk. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359 https://doi.org/10.32604/cmc.2019.06641
15
D GuanJ LiJ Chen. Optimization method of wavelet neural network for suspension bridge damage identification. In: Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering. Paris: Atlantis Press, 2016, 194–197
16
H Guo, X Zhuang, P Chen, N Alajlan, T Rabczuk. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 38(6): 5173–5198 https://doi.org/10.1007/s00366-021-01586-2
17
H Guo, X Zhuang, P Chen, N Alajlan, T Rabczuk. Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis. Engineering with Computers, 2022, 38(6): 5423–5444 https://doi.org/10.1007/s00366-022-01633-6
18
E Samaniego, C Anitescu, S Goswami, V M Nguyen-Thanh, H Guo, K Hamdia, X Zhuang, T Rabczuk. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790 https://doi.org/10.1016/j.cma.2019.112790
19
X Zhuang, H Guo, N Alajlan, H Zhu, T Rabczuk. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics A-Solids, 2021, 87: 104225 https://doi.org/10.1016/j.euromechsol.2021.104225
20
S Sony, K Dunphy, A Sadhu, M Capretz. A systematic review of convolutional neural network-based structural condition assessment techniques. Engineering Structures, 2021, 226: 111347 https://doi.org/10.1016/j.engstruct.2020.111347
21
B Ahmed, S Mangalathu, J S Jeon. Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks. Journal of Building Engineering, 2022, 46: 103737 https://doi.org/10.1016/j.jobe.2021.103737
22
B X Zhao, C M Cheng, Z K Peng, X J Dong, G Meng. Detecting the early damages in structures with nonlinear output frequency response functions and the CNN-LSTM model. IEEE Transactions on Instrumentation and Measurement, 2020, 69(12): 9557–9567 https://doi.org/10.1109/TIM.2020.3005113
23
X C Yang, H Li, Y T Yu, X C Luo, T Huang, X Yang. Automatic pixel-level crack detection and measurement using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1090–1109 https://doi.org/10.1111/mice.12412
24
Y Lin, Z Nie, H Ma. Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(12): 1025–1046 https://doi.org/10.1111/mice.12313
25
Y F Duan, Q Y Chen, H M Zhang, C B Yun, S K Wu, Q Zhu. CNN-based damage identification method of tied-arch bridge using spatial-spectral information. Smart Structures and Systems, 2019, 23(5): 507–520
26
Q H Han, X Liu, J Xu. Detection and location of steel structure surface cracks based on unmanned aerial vehicle images. Journal of Building Engineering, 2022, 50: 104098 https://doi.org/10.1016/j.jobe.2022.104098
27
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
28
J Kim, D Ho, K Nguyen, D Hong, S W Shin, C B Yun, M Shinozuka. System identification of a cable-stayed bridge using vibration responses measured by a wireless sensor network. Smart Structures and Systems, 2013, 11(5): 533–553 https://doi.org/10.12989/sss.2013.11.5.533
29
S O Sajedi, X Liang. Vibration-based semantic damage segmentation for large-scale structural health monitoring. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(6): 579–596 https://doi.org/10.1111/mice.12523
30
X Liang. Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(5): 415–430 https://doi.org/10.1111/mice.12425
31
J Rhim, S W Lee. A neural-network approach for damage detection and identification of structures. Computational Mechanics, 1995, 16(6): 437–443 https://doi.org/10.1007/BF00370565
32
Y Lei, Y Zhang, J Mi, W Liu, L Liu. Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data. Structural Health Monitoring, 2021, 20(4): 1583–1596 https://doi.org/10.1177/1475921720923081
33
S Cofre-Martel, P Kobrich, E Lopez Droguett, V Meruane. Deep convolutional neural network-based structural damage localization and quantification using transmissibility data. Shock and Vibration, 2019, 2019: 9859281 https://doi.org/10.1155/2019/9859281
34
J F Barraza, E L Droguett, V M Naranjo, M R Martins. Capsule Neural Networks for structural damage localization and quantification using transmissibility data. Applied Soft Computing, 2020, 97: 106732 https://doi.org/10.1016/j.asoc.2020.106732
35
Y Q Ni, B S Wang, J M Ko. Constructing input vectors to neural networks for structural damage identification. Smart Materials and Structures, 2002, 11(6): 825–833 https://doi.org/10.1088/0964-1726/11/6/301
36
W L QuW ChenQ S Li. Two-step approach for joints damage diagnosis of framed structures by artificial neural networks. China Civil Engineering Journal, 2003, 36(5): 37-45 (in Chinese)
37
N Fallah, S R H Vaez, A Mohammadzadeh. Multi-damage identification of large-scale truss structures using a two-step approach. Journal of Building Engineering, 2018, 19: 494–505 https://doi.org/10.1016/j.jobe.2018.06.007
38
X N Guo, J D Zhang, S J Zhu, X Q Luo, H J Xu. Damping characteristics of single-layer aluminum alloy reticulated spatial structures based on improved modal parameter identification method. Thin-walled Structures, 2021, 164: 107822 https://doi.org/10.1016/j.tws.2021.107822
39
A Krizhevsky, I Sutskever, G E Hinton. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84–90 https://doi.org/10.1145/3065386
40
K SimonyanA Zisserman. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015. San Diego, CA: MIT Press, 2015, 1–14
41
C SzegedyW LiuY Q JiaP SermanetS ReedD AnguelovD ErhanV VanhouckeA Rabinovich. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA: IEEE, 2015, 1–9
42
K M HeX Y ZhangS Q RenJ Sun. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016, 770–778
43
ANSYS. Version 18.0. Canonsburg, PA: Ansys Inc. 2017
44
A Géron. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd ed. Sebastopol, CA: O'Reilly Media, 2022
N Srivastava, G Hinton, A Krizhevsky, I Sutskever, R Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15: 1929–1958
47
D P KingmaJ Ba. Adam: A method for stochastic optimization. 2014, arXiv: 1412.6980
