[en] Structural damage detection using wavelet transform is effective if a suitable wavelet function is selected. However, the selection of the appropriate wavelet function can be a challenging task. This paper introduces a novel and efficient criterion for selecting the proper wavelet function. It is emphasized that there is a relationship between the accuracy of damage detection using wavelet detail coefficients and the correctness of the approximation of the original function. Accordingly, we define a ratio function and then a novel wavelet selection criterion called WSC in this study. After applying the proposed WSC criterion for 128 wavelet functions, it is found that the wavelet functions selected via the proposed criterion (both in numerical and experimental scenarios) can detect the position of damages in the beam structure with high accuracy.
Disciplines :
Computer science
Author, co-author :
Saadatmorad, Morteza ; Department of Mechanical engineering, Babol Noshirvani University of Technology, Iran
Khatir, Samir; Center for Engineering Application and Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Viet Nam
Cuong-Le, Thanh ; Center for Engineering Application and Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Viet Nam
Benaissa, Brahim; Department of Mechanical Systems Engineering, Design Engineering Lab, Toyota Technological Institute, Nagoya, Japan
Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
Detecting damages in metallic beam structures using a novel wavelet selection criterion
Nanthakumar, S.S., Lahmer, T., Zhuang, X., Zi, G., Rabczuk, T., Detection of material interfaces using a regularized level set method in piezoelectric structures. Inverse Probl. Sci. Eng. 24:1 (2016), 153–176.
Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V.M., Guo, H., Hamdia, K., Rabczuk, T., An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Comput. Methods Appl. Mech. Eng., 362, 2020, 112790.
Anitescu, C., Atroshchenko, E., Alajlan, N., Rabczuk, T., Artificial neural network methods for the solution of second order boundary value problems. Comput. Mater. Continua, 59(1), 2019.
Ouladbrahim, A., Belaidi, I., Khatir, S., Magagnini, E., Capozucca, R., Wahab, M.A., Experimental crack identification of API X70 steel pipeline using improved artificial neural networks based on whale optimization algorithm. Mech. Mater., 166, 2022, 104200.
Ghandourah, E., et al. Novel approach-based sparsity for damage localization in functionally graded material. Buildings, 13(7), 2023, 1768.
Ghandourah, E., Khatir, S., Banoqitah, E.M., Alhawsawi, A.M., Benaissa, B., Wahab, M.A., Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads. Buildings, 13(4), 2023, 973.
Benaissa, B., Khatir, S., Jouini, M.S., Riahi, M.K., Optimal axial-probe design for Foucault-current tomography: a global optimization approach based on linear sampling method. Energies, 16(5), 2023, 2448.
Alkayem, N.F., Cao, M., Zhang, Y., Bayat, M., Su, Z., Structural damage detection using finite element model updating with evolutionary algorithms: a survey. Neural Comput. Appl. 30 (2018), 389–411.
Das, S., Saha, P., Patro, S.K., Vibration-Based damage detection techniques used for health monitoring of structures: a review. J. Civ. Struct. Heal. Monit. 6 (2016), 477–507.
Fugate, M.L., Sohn, H., Farrar, C.R., Vibration-Based damage detection using statistical process control. Mech. Syst. Signal Process. 15:4 (2001), 707–721.
Kahouadji, A., Tiachacht, S., Slimani, M., Behtani, A., Khatir, S., Benaissa, B., Vibration-Based damage assessment in truss structures using local frequency change ratio indicator combined with metaheuristic optimization algorithms BT. Proceedings of the International Conference of Steel and Composite for Engineering Structures, 2023, 171–185.
Hester, D., González, A., A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle. Mech. Syst. Signal Process. 28 (2012), 145–166.
Saadatmorad, M., Jafari-Talookolaei, R.A., Pashaei, M.H., Khatir, S., Damage detection on rectangular laminated composite plates using wavelet based convolutional neural network technique. Compos. Struct., 278, 2021, 114656.
Wang, S., Li, J., Luo, H., Zhu, H., Damage identification in underground tunnel structures with wavelet based residual force vector. Eng. Struct. 178 (2019), 506–520.
Khatir, A., et al. A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam. Compos. Struct., 2023, 311–116803.
Khatir, S., Wahab, M.A., Benaissa, B., Köppen, M., Crack identification using eXtended IsoGeometric analysis and particle swarm optimization. Proceedings of the 7th International Conference on Fracture Fatigue and Wear: FFW 2018, 9-10 July 2018, Ghent University, Belgium, 2019, 210–222.
Minh, H.L., Khatir, S., Rao, R.V., Abdel Wahab, M., Cuong-Le, T., A variable velocity strategy particle swarm optimization algorithm (VVS-PSO) for damage assessment in structures. Eng. Comput. 39:2 (2023), 1055–1084.
Khatir, S., et al. Damage identification in steel plate using FRF and inverse analysis. Frat. ed integrita Strutt. Struct. Integr. 58 (2021), 416–433.
Khatir, S., Tiachacht, S., Benaissa, B., Le Thanh, C., Capozucca, R., Abdel Wahab, M., Damage identification in frame structure based on inverse analysis. Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment: SDMA 2021, 4–5 August, Ghent University, Belgium, 2022, 197–211.
Heshmati, A., Saadatmorad, M., Talookolaei, R.-A.J., Valvo, P.S., Khatir, S., Damage identification in thin steel beams containing a horizontal crack using the artificial neural networks. Proceedings of the International Conference of Steel and Composite for Engineering Structures, 2022, 114–126.
Cuong-Le, T., et al. A novel version of grey wolf optimizer based on a balance function and its application for hyperparameters optimization in deep neural network (DNN) for structural damage identification. Eng. Fail. Anal., 142, 2022, 106829.
Slimani, M., et al. Improved ANN for damage identification in laminated composite plate BT. Proceedings of the International Conference of Steel and Composite for Engineering Structures, 2023, 186–198.
Amoura, N., Benaissa, B., Al Ali, M., Khatir, S., Deep neural network and YUKI algorithm for inner damage characterization based on elastic boundary displacement BT. Proceedings of the International Conference of Steel and Composite for Engineering Structures, 2023, 220–233.
Irfan Shirazi, M., Khatir, S., Benaissa, B., Mirjalili, S., Abdel Wahab, M., Damage assessment in laminated composite plates using modal strain energy and YUKI-ANN algorithm. Compos. Struct., 303, 2023, 116272, 10.1016/j.compstruct.2022.116272.
Slimani, M., Khatir, T., TIACHACHT, S., BOUTCHICHA, D., Benaissa, B., Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite. J. Mater. Eng. Struct. «JMES» 9:2 (2022), 207–220.
Gholizadeh, S., A review of non-destructive testing methods of composite materials. Procedia Struct. Integr. 1 (2016), 50–57.
Hou, R., Xia, Y., Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019. J. Sound Vib., 491, 2021, 115741.
Yam, L.H., Yan, Y.J., Jiang, J.S., Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Compos. Struct. 60:4 (2003), 403–412.
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J., A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech. Syst. Signal Process., 147, 2021, 107077.
Sun, G., Wang, Y., Luo, Q., Li, Q., Vibration-based damage identification in composite plates using 3D-DIC and wavelet analysis. Mech. Syst. Signal Process., 173, 2022, 108890.
Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods. Shock Vib. Dig. 30:2 (1998), 91–105.
Saadatmorad, M., Jafari-Talookolaei, R.A., Pashaei, M.H., Khatir, S., Damage detection in rectangular laminated composite plate structures using a combination of wavelet transforms and artificial neural networks. J. Vib. Eng. Technol. 10:5 (2022), 1647–1664.
Fan, W., Qiao, P., Vibration-based damage identification methods: a review and comparative study. Struct. Heal. Monit. 10:1 (2011), 83–111.
Wahab, M.M.A., De Roeck, G., Damage detection in bridges using modal curvatures: application to a real damage scenario. J. Sound Vib. 226:2 (1999), 217–235.
Ramos, L.F., Marques, L., Lourenço, P.B., De Roeck, G., Campos-Costa, A., Roque, J., Monitoring historical masonry structures with operational modal analysis: two case studies. Mech. Syst. Signal Process. 24:5 (2010), 1291–1305.
Ratcliffe, C.P., Damage detection using a modified Laplacian operator on mode shape data. J. Sound Vib. 204:3 (1997), 505–517.
Yazdanpanah1a, O., Seyedpoor, S.M., A new damage detection indicator for beams based on mode shape data. Struct. Eng. Mech. 53:4 (2015), 725–744.
Abdo, M.B., Hori, M., A numerical study of structural damage detection using changes in the rotation of mode shapes. J. Sound Vib. 251:2 (2002), 227–239.
