An efficient approach for damage identification based on improved machine learning using PSO-SVM


Structural health monitoring (SHM) and Non-destructive Damage Identification (NDI) using responses of structures under dynamic excitation have an imperative role in the engineering application to make the structures safe. Interpretations of structural responses known as inverse problems are emerging topics with a large body of works in the literature. They have been widely solved with Machine Learning (ML) techniques such as Artificial Neural Network (ANN), Deep Neural Network (DNN), Adaptive Network-based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). Nonetheless, these approaches can precisely predict the inverse problems of civil structures (e.g., truss or frame systems) with low damage levels, which have to wait until the structures reach certain damage or deteriorate level. The issue is related to the fact that most of the real structures have very low damage levels during their routine maintenances and usually be neglected due to limitations of the current techniques. This paper proposes a combination of Particle Swarm Optimization and Support Vector Machine (PSO-SVM) for damage identifications. The proposed approach is inspired by the effective searching capability of PSO, which can eliminate the redundant input parameters and robust SVM technique to classify damage locations effectively. In other words, natural frequencies and mode shapes extracted from the numerical examples of truss and frame structures are used as input parameters in which the redundant parameters might lead to reduction of the accuracy in the predicting models. The proposed PSO-SVM shows superior accuracy prediction in both damage locations and damage levels compared to the other ML models. It also substantially outperforms other ML models through validated cases of low damage levels.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17


  1. 1.

    Khatir S, Boutchicha D, Le Thanh C, Tran-Ngoc H, Nguyen TN, Abdel-Wahab M (2020) Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis. Theoret Appl Fract Mech 107:102554

    Article  Google Scholar 

  2. 2.

    Khatir S, Dekemele K, Loccufier M, Khatir T, Abdel Wahab M (2018) Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization. Comptes Rendus Mécanique 346(2):110–120

    Article  Google Scholar 

  3. 3.

    Khatir S, Tiachacht S, Thanh CL, Bui TQ, Abdel Wahab M (2019) Damage assessment in composite laminates using ANN-PSO-IGA and Cornwell indicator. Compos Struct 230:111509

    Article  Google Scholar 

  4. 4.

    Tiachacht S, Bouazzouni A, Khatir S, Abdel Wahab M, Behtani A, Capozucca R (2018) Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm. Eng Struct 177:421–430

    Article  Google Scholar 

  5. 5.

    Samir K, Brahim B, Capozucca R, Abdel Wahab M (2018) Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm. Compos Struct 187:344–353

    Article  Google Scholar 

  6. 6.

    Ding Z, Li J, Hao H (2019) Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference. Mech Syst Signal Process 132:211–231

    Article  Google Scholar 

  7. 7.

    Ni YC, Yun YC, Zhang QW, Lu KC (2020) Vibration properties monitoring and uncertainty quantification of precast and cast‐in‐situ shear wall buildings using a Bayesian framework. Struct Control Health Monit 27(6):e2537

    Article  Google Scholar 

  8. 8.

    Zheng X, Yang DH, Yi TH, Li HN (2020) Bridge influence line identification from structural dynamic responses induced by a high‐speed vehicle. Struct Control Health Monit 27(7):e2544

    Article  Google Scholar 

  9. 9.

    Zhang C, Wang H (2020) Swing vibration control of suspended structures using the Active Rotary Inertia Driver system: Theoretical modeling and experimental verification. Struct Control Health Monit 27(6):e2543

    Google Scholar 

  10. 10.

    Agis D, Tibaduiza DA, Pozo F (2020) Vibration-based detection and classification of structural changes using principal component analysis and -distributed stochastic neighbor embedding. Struct Control Health Monit 27(6):e2533

    Article  Google Scholar 

  11. 11.

    Todorovska MI et al (2020) A new full‐scale testbed for structural health monitoring and soil–structure interaction studies: Kunming 48‐story office building in Yunnan province, China. Struct Control Health Monit 27(7):e2545

    Google Scholar 

  12. 12.

    Zenzen R, Belaidi I, Khatir S, Abdel Wahab M (2018) A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm. Comptes Rendus Mécanique 346(12):1253–1266

    Article  Google Scholar 

  13. 13.

    Khatir S, Abdel Wahab M (2019) A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm. Theoret Appl Fracture Mech 103:102240

    Article  Google Scholar 

  14. 14.

    Khatir S, Abdel Wahab M, Boutchicha D, Khatir T (2019) Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis. J Sound Vib 448:230–246

    Article  Google Scholar 

  15. 15.

    Kang J, Liu L, Zhou S-D, Wang D-Y, Ma Y-C (2020) A novel recursive modal parameter estimator for operational time-varying structural dynamic systems based on least squares support vector machine and time series model. Comput Struct 229:106173

    Article  Google Scholar 

  16. 16.

    Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  17. 17.

    Das S, Nayak B, Sarangi SK, Biswal DK (2016) Condition monitoring of robust damage of cantilever shaft using experimental and adaptive neuro-fuzzy inference system (ANFIS). Proc Eng 144:328–335

    Article  Google Scholar 

  18. 18.

    Kouziokas GN (2020) A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting. Eng Appl Artif Intell 92:103650

    Article  Google Scholar 

  19. 19.

    Vasumathi B, Moorthi S (2012) Implementation of hybrid ANN–PSO algorithm on FPGA for harmonic estimation. Eng Appl Artif Intell 25(3):476–483

    Article  Google Scholar 

  20. 20.

    Taheri Shahraiyni H, Sodoudi S, Kerschbaumer A, Cubasch U (2015) A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas. Eng Appl Artif Intell 41:175–182

    Article  Google Scholar 

  21. 21.

    Al-Dunainawi Y, Abbod MF, Jizany A (2017) A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems. Eng Appl Artif Intell 62:265–275

    Article  Google Scholar 

  22. 22.

    Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using artificial neural networks. Expert Syst Appl 35(3):1122–1131

    Article  Google Scholar 

  23. 23.

    Gang X, Chai S, Allemang RJ, Li L (2014) A new iterative model updating method using incomplete frequency response function data. J Sound Vib 333(9):2443–2453

    Article  Google Scholar 

  24. 24.

    Esfandiari A, Bakhtiari-Nejad F, Rahai A, Sanayei M (2009) Structural model updating using frequency response function and quasi-linear sensitivity equation. J Sound Vib 326(3–5):557–573

    Article  Google Scholar 

  25. 25.

    Ahn G, Hur S (2020) Efficient genetic algorithm for feature selection for early time series classification. Comput Ind Eng 142:106345

    Article  Google Scholar 

  26. 26.

    Li A-D, Xue B, Zhang M (2020) Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Inf Sci 523:245–265

    MathSciNet  Article  Google Scholar 

  27. 27.

    Sayed S, Nassef M, Badr A, Farag I (2019) A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets. Expert Syst Appl 121:233–243

    Article  Google Scholar 

  28. 28.

    Amoozegar M, Minaei-Bidgoli B (2018) Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism. Expert Syst Appl 113:499–514

    Article  Google Scholar 

  29. 29.

    Lin Y-Z, Nie Z-H, Ma H-W (2017) Structural damage detection with automatic feature-extraction through deep learning. Comput-Aided Civ Infrastruct Eng 32(12):1025–1046

    Article  Google Scholar 

  30. 30.

    Nguyen LC, Nguyen-Xuan H (2020) Deep learning for computational structural optimization. ISA Trans 103:177–191

    Article  Google Scholar 

  31. 31.

    Hamdia KM, Zhuang X, Rabczuk T (2020) An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Comput Appl 2020:1–11

    Google Scholar 

  32. 32.

    Nguyen TQ, Vuong LC, Le CM, Ngo NK, Nguyen- Xuan H (2020) A data-driven approach based on wavelet analysis and deep learning for identification of multiple-cracked beam structures under moving load. Measurement 162:107862

    Article  Google Scholar 

  33. 33.

    Hakim SJS, Razak HA (2013) Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification. Struct Eng Mech 45(6):779–802

    Article  Google Scholar 

  34. 34.

    Escamilla-Ambrosio PJ, Liu X, Lieven NAJ, Ramírez-Cortés JM (2011) ANFIS-2D wavelet transform approach to structural damage identification. In: 2011 Annual Meeting of the North American fuzzy information processing society, 2011, pp 1–6

  35. 35.

    Chen B, Wu Z, Liang J, Dou Y (2017) Time-varying identification model for crack monitoring data from concrete dams based on support vector regression and the bayesian framework. Math Probl Eng 2017:1–11

    Google Scholar 

  36. 36.

    Satpal SB, Guha A, Banerjee S (2016) Damage identification in aluminum beams using support vector machine: numerical and experimental studies. Struct Control and Health Monit 23(3):446–457

    Article  Google Scholar 

  37. 37.

    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95—International Conference on neural networks, 1995, vol. 4, pp. 1942–1948

  38. 38.

    Li Z-X, Yang X-M (2008) Damage identification for beams using ANN based on statistical property of structural responses. Comput Struct 86(1–2):64–71

    Article  Google Scholar 

  39. 39.

    Jayasundara N, Thambiratnam DP, Chan THT, Nguyen A (2019) Damage detection and quantification in deck type arch bridges using vibration based methods and artificial neural networks. Eng Fail Anal 109:104265

    Article  Google Scholar 

Download references


The authors would like to acknowledge the support from Ho Chi Minh City Open University under the basic research fund (No. E2019.10.3).

Author information



Corresponding authors

Correspondence to Thanh Cuong-Le or Trong Nghia-Nguyen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cuong-Le, T., Nghia-Nguyen, T., Khatir, S. et al. An efficient approach for damage identification based on improved machine learning using PSO-SVM. Engineering with Computers (2021).

Download citation


  • Damage identifications
  • Truss structure
  • 3D frame structure
  • ANN
  • DNN