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Inverse structural damage identification problem in CFRP laminated plates using SFO algorithm based on strain fields

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Abstract

Damage detection methods are an important field of engineering and crucial in terms of structural safety. However, in many practical cases, the process of monitoring and identifying damage is extremely difficult or even impractical due to the conditions of access and operation of a given component/structure. In this study, an inverse algorithm based on strain fields for damage identification in composite plate structures is presented. The inverse analyses combine experimental tests and digital image correlation (DIC) with numerical models based on finite element update method with great advantage of being a non-contact method. The proposed technique identifies the location and dimension of damages in a CFRP plate using static strains formulated as an objective function to be minimized. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. A powerful new metaheuristic sunflower optimization (SFO) is employed to update the unknown model parameters. Experimental results showed the excellent efficiency in the combined use of DIC, numerical modeling and SFO optimization to accurately identify the location of damage in numerical and experimental tests. The obtained results indicate that the proposed method can be used to determine efficiently the location and dimension of structural damages in mechanical structures.

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References

  1. Balageas D, Fritzen C-P, Güemes A (2010) Structural health monitoring, vol 90. Wiley, Hoboken

    Google Scholar 

  2. Feng D, Feng MQ (2017) Experimental validation of cost-effective vision-based structural health monitoring. Mech Syst Signal Process 88:199–211

    Google Scholar 

  3. Gomes GF, Mendéz YAD, da Silva Lopes Alexandrino P, da Cunha SS Jr, Ancelotti AC Jr (2018) The use of intelligent computational tools for damage detection and identification with an emphasis on composites – A review. Compos Struct 196:44–54. https://doi.org/10.1016/j.compstruct.2018.05.002

    Article  Google Scholar 

  4. Samir K, Brahim B, Capozucca R, Wahab MA (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

    Google Scholar 

  5. de Sousa BS, Gomes GF, Jorge AB, da Cunha SS Jr, Ancelotti AC Jr (2018) A modified topological sensitivity analysis extended to the design of composite multidirectional laminates structures. Compos Struct 200:729–746

    Google Scholar 

  6. de Souza A, Gomes GF, Peres EP, Isaías JC, Ancelotti AC (2019) A numerical-experimental evaluation of the fatigue strain limits of cfrp subjected to dynamic compression loads. Int J Adv Manuf Technol 103(1–4):219–237

    Google Scholar 

  7. Di Benedetto RM, Botelho EC, Gomes GF, Junqueira DM, Ancelotti Junior AC (2019) Impact energy absorption capability of thermoplastic commingled composites. Compos B Eng 176:107307

    Google Scholar 

  8. Bhudolia SK, Perrotey P, Joshi SC (2018) Mode i fracture toughness and fractographic investigation of carbon fibre composites with liquid methylmethacrylate thermoplastic matrix. Compos B Eng 134:246–253

    Google Scholar 

  9. Diniz CA, Cunha SS, Gomes GF, Ancelotti AC (2019) Optimization of the layers of composite materials from neural networks with tsai-wu failure criterion. J Fail Anal Prev 19(3):709–715

    Google Scholar 

  10. Gomes GF, Mendéz YAD, Simões S, da Cunha, Antônio CA (2018) A numerical-experimental study for structural damage detection in cfrp plates using remote vibration measurements. J Civ Struct Health Monit 8(1):33–47

    Google Scholar 

  11. da Silva P, Alexandrino L, Gomes GF, Jr Sebastião Simões C (2020) A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making. Inverse Prob Sci Eng 28(1):21–46

  12. Heslehurst RB (2014) Defects and damage in composite materials and structures. CRC Press, Boca Raton

    Google Scholar 

  13. Gomes GF, de Almeida FA, da Silva Lopes Alexandrino P, da Cunha SS Jr, de Sousa BS, Ancelotti AC Jr (2018) A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation. Eng Comput 35(2):519–535. https://doi.org/10.1007/s00366-018-0613-7

    Article  Google Scholar 

  14. Gopalakrishnan S, Ruzzene M, Hanagud S (2011) Computational techniques for damage detection, classification and quantification. In: Computational techniques for structural health monitoring. Springer, New York, pp 407–461

  15. Yun-Lai Z, Maia Nuno MM, Sampaio Rui PC, Abdel WM (2017) Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure. Struct Health Monit 16(6):711–731

    Google Scholar 

  16. Gillich G-R, Furdui H, Wahab MA, Korka Z-I (2019) A robust damage detection method based on multi-modal analysis in variable temperature conditions. Mech Syst Signal Process 115:361–379

    Google Scholar 

  17. Zhou Y-L, Wahab MA (2017) Cosine based and extended transmissibility damage indicators for structural damage detection. Eng Struct 141:175–183

    Google Scholar 

  18. Zhou Y-L, Maia NMM, Wahab MA (2018) Damage detection using transmissibility compressed by principal component analysis enhanced with distance measure. J Vib Control 24(10):2001–2019

    Google Scholar 

  19. Khatir S, Wahab MA, 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

    Google Scholar 

  20. Ribeiro Junior RF, de Almeida FA, Gomes GF (2020) Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04868-w

    Article  Google Scholar 

  21. Barbosa LCM, Santos M, Oliveira TLL, Gomes GF, Ancelotti AC Jr (2019) Effects of moisture absorption on mechanical and viscoelastic properties in liquid thermoplastic resin/carbon fiber composites. Polymer Eng Sci 59(11):2185–2194

    Google Scholar 

  22. Barbosa LCM, Gomes G, Junior ACA (2019) Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks. Int J Adv Manuf Technol 105(5–6):2543–2556

    Google Scholar 

  23. Chandarana N, Sanchez D, Soutis C, Gresil M (2017) Early damage detection in composites during fabrication and mechanical testing. Materials 10(7):685

    Google Scholar 

  24. Kessler SS, Mark Spearing S, Atalla MJ, Cesnik CES, Soutis C (2002) Damage detection in composite materials using frequency response methods. Compos B Eng 33(1):87–95

    Google Scholar 

  25. Cantwell WJ, Morton J (1992) The significance of damage and defects and their detection in composite materials: a review. J Strain Anal Eng Des 27(1):29–42

    Google Scholar 

  26. Pearson MR, Eaton MJ, Featherston CA, Holford KM, Pullin R (2011) Impact damage detection and assessment in composite panels using macro fibre composites transducers. J Phys Conf Ser 305:012049

    Google Scholar 

  27. Gomes GF, Pereira JVP (2020) Sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm. Evol Intell 1–21

  28. François H, Jean-Noël P, Stéphane R (2015) Evaluating damage with digital image correlation: C. applications to composite materials. In: Handbook of damage mechanics: nano to macro scale for materials and structures, pp 1301–1322

  29. Caminero MA, Lopez-Pedrosa M, Pinna C, Soutis C (2014) Damage assessment of composite structures using digital image correlation. Appl Compos Mater 21(1):91–106

    Google Scholar 

  30. Fister Jr I, Yang X-S, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186

  31. Yang X-S, Xingshi H (2016) Nature-inspired optimization algorithms in engineering: overview and applications. Nature-inspired computation in engineering. Springer, New York, pp 1–20

    Google Scholar 

  32. Gomes GF, Mendez YAD, Alexandrino Patrícia da Silva L, da Cunha SS, Ancelotti AC (2018) A review of vibration based inverse methods for damage detection and identification in mechanical structures using optimization algorithms and ann.In: Archives of computational methods in engineering, pp 1–15

  33. Zenzen R, Belaidi I, Khatir S, Wahab MA (2018) A damage identification technique for beam-like and truss structures based on frf and bat algorithm. Comptes Rendus Mécanique 346(12):1253–1266

    Google Scholar 

  34. Tran-Ngoc H, De Samir Khatir G, Roeck T, Bui-Tien LN-N, Wahab MA (2018) Model updating for nam o bridge using particle swarm optimization algorithm and genetic algorithm. Sensors 18(12):4131

    Google Scholar 

  35. Khatir S, Abdel Wahab M (2019) Fast simulations for solving fracture mechanics inverse problems using pod-rbf xiga and jaya algorithm. Eng Fract Mech 205:285–300

    Google Scholar 

  36. Samir Khatir and Magd Abdel Wahab (2019) A computational approach for crack identification in plate structures using xfem, xiga, pso and jaya algorithm. Theor Appl Fract Mech 103:102240

    Google Scholar 

  37. Gomes GF, Simões S, da Cunha A, Ancelotti C (2019) A sunflower optimization (sfo) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626

    Google Scholar 

  38. Caminero MA, Pavlopoulou S, Lpez-Pedrosa M, Nicolaisson BG, Pinna C, Soutis C (2012) Digital image correlation analysis applied to monitor damage evolution of composite plates with stress concentrations and bonded patch repairs. In: Proceedings of the 15th European conference on composite materials, Venice, Italy, pp 24–28

  39. Memmolo V, Monaco E, Boffa ND, Maio L, Ricci F (2018) Guided wave propagation and scattering for structural health monitoring of stiffened composites. Compos Struct 184:568–580

    Google Scholar 

  40. Zuo H, Yang Z, Xu C, Tian S, Chen X (2018) Damage identification for plate-like structures using ultrasonic guided wave based on improved MUSIC method. Compos Struct 203:164–171. https://doi.org/10.1016/j.compstruct.2018.06.100

    Article  Google Scholar 

  41. Yang Z-B, Radzienski M, Kudela P, Ostachowicz W (2017) Damage detection in beam-like composite structures via chebyshev pseudo spectral modal curvature. Compos Struct 168:1–12

    Google Scholar 

  42. Xingwu Z, Gao Robert X, Ruqiang Y, Xuefeng C, Chuang S, Zhibo Y (2016) Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization. J Sound Vib 375:200–216

    Google Scholar 

  43. Yang Z-B, Radzienski M, Kudela P, Ostachowicz W (2017) Fourier spectral-based modal curvature analysis and its application to damage detection in beams. Mech Syst Signal Process 84:763–781

    Google Scholar 

  44. Stepinski T, Uhl T, Staszewski W (2013) Advanced structural damage detection: from theory to engineering applications. Wiley, Hoboken

    Google Scholar 

  45. Worden K, Staszewski W, Manson G, Ruotulo A, Surace C (2008) Optimization techniques for damage detection. In: Encyclopedia of structural health monitoring. Wiley. https://doi.org/10.1002/9780470061626.shm057

  46. Rytter A (1993) Vibrational based inspection of civil engineering structures. PhD thesis, Dept. of Building Technology and Structural Engineering, Aalborg University

  47. Shi D, Xiao X (2018) An enhanced continuum damage mechanics model for crash simulation of composites. Compos Struct 185:774–785

    Google Scholar 

  48. Soriano A, Díaz J (2018) Failure analysis of variable stiffness composite plates using continuum damage mechanics models. Compos Struct 184:1071–1080

    Google Scholar 

  49. Ben Sghaier R, Majed N, Ben Dali H, Fathallah R (2017) High cycle fatigue prediction of glass fiber-reinforced epoxy composites: reliability study. Int J Adv Manuf Technol 92(9–12):4399–4413

    Google Scholar 

  50. Sundararaman S, Adams DE, Rigas EJ (2005) Structural damage identification in homogeneous and heterogeneous structures using beamforming. Struct Health Monit 4(2):171–190

    Google Scholar 

  51. Reddy JN, Miravete A (2018) Practical analysis of composite laminates. CRC Press, Boca Raton

    Google Scholar 

  52. Sridharan S (2008) Delamination behaviour of composites. Elsevier, Amsterdam

    Google Scholar 

  53. Niemann H, Morlier J, Shahdin A, Gourinat Y (2010) Damage localization using experimental modal parameters and topology optimization. Mech Syst Signal Process 24(3):636–652

    Google Scholar 

  54. Montalvao D, Maia NMM, Ribeiro AMR (2006) A review of vibration-based structural health monitoring with special emphasis on composite materials. Shock Vib Digest 38(4):295–324

    Google Scholar 

  55. Zou Y, Tong LPSG, Steven GP (2000) Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures—a review. J Sound Vib 230(2):357–378

    Google Scholar 

  56. Liu PF, Zheng JY (2010) Recent developments on damage modeling and finite element analysis for composite laminates: A review. Mater Des 31(8):3825–3834

    Google Scholar 

  57. Chao X, Qi L, Cheng J, Tian W, Zhang S, Li H (2018) Numerical evaluation of the effect of pores on effective elastic properties of carbon/carbon composites. Compos Struct 196:108–116

    Google Scholar 

  58. Drach B, Tsukrov I, Trofimov A, Gross T, Drach A (2018) Comparison of stress-based failure criteria for prediction of curing induced damage in 3d woven composites. Compos Struct 189:366–377

    Google Scholar 

  59. Sokolnikoff IS (1956) Mathematical theory of elasticity. McGraw-Hill Book Company, New York

    MATH  Google Scholar 

  60. Malvern LE (1969) Introduction to the mechanics of a continuous medium (No. Monograph)

  61. Ugural Ansel C, Fenster Saul K (2011) Advanced mechanics of materials and applied elasticity. Pearson Education, London

    Google Scholar 

  62. Pilkey Walter D, Pilkey Deborah F (2008) Peterson’s stress concentration factors. Wiley, Hoboken

    Google Scholar 

  63. Carlos AJA, Claudio PL, Marcelo BE, Dennis R (2010) Use of the mar-lin criteria to determine the influence of porosity on the iosipescu and short beam shear properties in carbon fiber polymer matrix composites. Mater Res 13(1):63–69

    Google Scholar 

  64. Ye L, Afaghi-Khatibi A, Lawcock G, Mai Y-W (1998) Effect of fibre/matrix adhesion on residual strength of notched composite laminates. Compos A Appl Sci Manuf 29(12):1525–1533

    Google Scholar 

  65. Tan Seng C (1994) Stress concentrations in laminated composites. CRC Press, Boca Raton

    Google Scholar 

  66. Chu TC, Ranson WF, Sutton MA (1985) Applications of digital-image-correlation techniques to experimental mechanics. Exp Mech 25(3):232–244

    Google Scholar 

  67. Orell O, Vuorinen J, Jokinen J, Kettunen H, Hytönen P, Turunen J, Kanerva M (2018) Characterization of elastic constants of anisotropic composites in compression using digital image correlation. Compos Struct 185:176–185

    Google Scholar 

  68. Tekieli M, De Santis S, de Felice G, Kwiecień A, Roscini F (2017) Application of digital image correlation to composite reinforcements testing. Compos Struct 160:670–688

    Google Scholar 

  69. Peters WH, Ranson WF (1982) Digital imaging techniques in experimental stress analysis. Opt Eng 21(3):213427

    Google Scholar 

  70. Sutton MA, Wolters WJ, Peters WH, Ranson WF, McNeill SR (1983) Determination of displacements using an improved digital correlation method. Image Vis Comput 1(3):133–139

    Google Scholar 

  71. Beberniss TJ, Ehrhardt DA (2017) High-speed 3d digital image correlation vibration measurement: Recent advancements and noted limitations. Mech Syst Signal Process 86:35–48

    Google Scholar 

  72. Crammond G, Boyd SW, Dulieu-Barton JM (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378

    Google Scholar 

  73. Johanson K, Harper LT, Johnson MS, Warrior NA (2015) Heterogeneity of discontinuous carbon fibre composites: damage initiation captured by digital image correlation. Compos A Appl Sci Manuf 68:304–312

    Google Scholar 

  74. Speranzini E, Agnetti S (2014) The technique of digital image correlation to identify defects in glass structures. Struct Control Health Monit 21(6):1015–1029

    Google Scholar 

  75. Yoneyama S, Murasawa G (2009) Digital image correlation. Exp Mech 207

  76. Gomes GF, da Cunha SS Jr, da Silva Lopes Alexandrino P, Silva de Sousa B, Ancelotti AC Jr (2018) Sensor placement optimization applied to laminated composite plates under vibration. Struct Multi Optim 58(5):2099–2118. https://doi.org/10.1007/s00158-018-2024-1

    Article  MathSciNet  Google Scholar 

  77. Khatir S, Dekemele K, Loccufier M, Khatir T, Wahab MA (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

    Google Scholar 

  78. Braun CE, Chiwiacowsky LD, Gomez AT (2015) Variations of ant colony optimization for the solution of the structural damage identification problem. Procedia Comput Sci 51:875–884

    Google Scholar 

  79. Kim N-I, Kim S, Lee J (2019) Vibration-based damage detection of planar and space trusses using differential evolution algorithm. Appl Acoust 148:308–321

    Google Scholar 

  80. Bayraktar Z, Komurcu M, Bossard JA, Werner DH (2013) The wind driven optimization technique and its application in electromagnetics. IEEE Trans Antennas Propag 61(5):2745–2757

    MathSciNet  MATH  Google Scholar 

  81. Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, New York, pp 240–249

  82. Richards SHANEA (1997) Completed richardson extrapolation in space and time. Commun Numer Methods Eng 13(7):573–582

    MathSciNet  MATH  Google Scholar 

  83. Robert Frank G (2007) Sensor placement optimization under uncertainty for structural health monitoring systems of hot aerospace structures. PhD thesis, Citeseer

  84. Ray-Chaudhuri S, Chawla K (2018) Stress and strain concentration factors in orthotropic composites with hole under uniaxial tension. Curved Layer Struct 5(1):213–231

    Google Scholar 

  85. Perumal L, Tso CP, Leng LT (2016) Analysis of thin plates with holes by using exact geometrical representation within xfem. J Adv Res 7(3):445–452

    Google Scholar 

  86. Taynara Incerti de Paula, Guilherme FG, José Henrique de Freitas G, Anderson Paulo de Paiva (2019) A mixture design of experiments approach for genetic algorithm tuning applied to multi-objective optimization. In: World Congress on Global Optimization. Springer, New York, pp 600–610

  87. Qais MH, Hasanien HM, Alghuwainem S (2019) Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm. Appl Energy 250:109–117

    Google Scholar 

  88. Shaheen MAM, Hasanien HM, Mekhamer SF, Talaat HEA (2019) Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm. IEEE Access 7:109289–109300

    Google Scholar 

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Acknowledgements

The authors would like to acknowledge the financial support from the Brazilian agency CNPq - Conselho Nacional de Desenvolvimento Cientııfico e Tecnolígico, CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nııvel Superior and FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais (APQ-00385-18). The authors would like to acknowledge also the Tutorial Education Program (PET)

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Gomes, G.F., de Almeida, F.A., Ancelotti, A.C. et al. Inverse structural damage identification problem in CFRP laminated plates using SFO algorithm based on strain fields. Engineering with Computers 37, 3771–3791 (2021). https://doi.org/10.1007/s00366-020-01027-6

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