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A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm

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Abstract

In this study, an efficient three-stage method is proposed for damage detection of large-scale space structures by employing forward substructuring approach, modal strain energy and enhanced bat algorithm (EBA) optimization. EBA is a modified version of BA that is proposed in this paper and used a passive congregation operator to improve the performance of standard BA. In the first stage, the global structure is divided into manageable substructures. The stiffness matrices of independent substructures are obtained based on Kron’s substructuring method. Then a modal strain energy-based index is employed to precisely locate the eventual damages of the structure. In the third stage, damage severities are estimated via EBA using the second-stage results. To demonstrate the ability of the proposed method for detection of multiple structural damages, large-scale space structures with different types of damage scenarios are considered. The results show that the proposed method can detect the exact locations and severity of damages highly accurate in space structures.

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References

  1. Beygzadeh S, Salajegheh E, Torkzadeh P, Salajegheh J, Naseralavi S (2014) An improved genetic algorithm for optimal sensor placement in space structures damage detection. Int J Space Struct 29:121–136

    Article  Google Scholar 

  2. Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):303–315

    Article  Google Scholar 

  3. Goyal D, Pabla BS (2015) The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch Comput Methods Eng 23(4):585–594. https://doi.org/10.1007/s11831-015-9145-0

    Article  MathSciNet  MATH  Google Scholar 

  4. Fan W, Qiao P (2011) Vibration-based damage identification methods: a review and comparative study. Struct Heal Monit 10(1):83–111

    Article  Google Scholar 

  5. Naseralavi SS, Salajegheh J, Salajegheh E, Fadaee MJ (2010) An improved genetic algorithm using sensitivity analysis and micro search for damage detection. Asian J Civ Eng v11 i6:717–740

    MATH  Google Scholar 

  6. Kim J-B, Lee E-T, Rahmatalla S, Eun H-C (2013) Non-baseline damage detection based on the deviation of displacement mode shape data. J Nondestruct Eval 32(1):14–24

    Article  Google Scholar 

  7. Yoon MK, Heider D, Gillespie JW, Ratcliffe CP, Crane RM (2010) Local damage detection with the global fitting method using operating deflection shape data. J Nondestruct Eval 29(1):25–37

    Article  Google Scholar 

  8. Guo HY, Li ZL (2011) Two-stage multi-damage detection method based on energy balance equation. J Nondestruct Eval 30(3):186–200

    Article  Google Scholar 

  9. Amiri GG, Hosseinzadeh AZ, Razzaghi SAS (2015) Generalized flexibility-based model updating approach via democratic particle swarm optimization algorithm for structural damage. Int J Optim Civ Eng 5:445–464

    Google Scholar 

  10. Monajemi H, Razak HA, Ismail Z (2013) Damage detection in frame structures using damage locating vectors. Measurement 46:3541–3548

    Article  Google Scholar 

  11. Varmazyar M, Haritos N (2015) A Bayesian damage identification technique using evolutionary algorithms—a comparative study. Electron J Struct Eng 14(1):1–19

    Google Scholar 

  12. Alampalli S (1998) Influence of in-service environment on modal parameters. Proc Spie Int Soc Opt Eng 1:111–116

    Google Scholar 

  13. Maeck J, De Roeck G (2003) Damage assessment using vibration analysis on the Z24-bridge. Mech Syst Signal Process 17(1):133–142

    Article  Google Scholar 

  14. Robert GL, John TD (2006) Ambient vibration monitoring of a highway bridge undergoing a destructive test. J Bridg Eng 11(5):602–610

    Article  Google Scholar 

  15. Xia Y, Hao H, Zanardo G, Deeks A (2006) Long term vibration monitoring of an RC slab: temperature and humidity effect. Eng Struct 28(3):441–452

    Article  Google Scholar 

  16. Torkzadeh P, Goodarzi Y, Salajegheh E (2013) A two-stage damage detection method for large-scale structures by kinetic and modal strain energies using heuristic particle swarm optimization. Int J Optim Civ Eng 3(3):465–482

    Google Scholar 

  17. Craig RR (2000) Coupling of substructures for dynamic analyses: an overview. AIAA Pap 1573:2000

    Google Scholar 

  18. Weng S, Xia Y, Xu Y-L, Zhu H-P (2011) Substructure based approach to finite element model updating. Comput Struct 89(9):772–782

    Article  Google Scholar 

  19. Weng S, Xia Y, Xu Y-L, Zhu H-P (2011) An iterative substructuring approach to the calculation of eigensolution and eigensensitivity. J Sound Vib 330(14):3368–3380

    Article  Google Scholar 

  20. Xia Y, Weng S, Xu Y-L, Zhu H-P (2010) Calculation of eigenvalue and eigenvector derivatives with the improved Kron’s substructuring method. Struct Eng Mech 36(1):37–55

    Article  Google Scholar 

  21. Ghiasi R, Torkzadeh P, Noori M (2016) A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct Heal Monit 15(3):302–316

    Article  Google Scholar 

  22. Fathnejat H, Torkzadeh P, Salajegheh E, Ghiasi R (2014) Structural damage detection by model updating method based on cascade feed-forward neural network as an efficient approximation mechanism. Int J Optim Civ Eng 4(4):451–472

    Google Scholar 

  23. Ghiasi R, Ghasemi MR, Noori M (2018) Comparative studies of metamodeling and AI-based techniques in damage detection of structures. Adv Eng Softw

  24. Gholizadeh S, Seyedpoor SM (2011) Shape optimization of arch dams by metaheuristics and neural networks for frequency constraints. Sci Iran 18(5):1020–1027

    Article  Google Scholar 

  25. Gholizadeh S, Shahrezaei AM (2015) Optimal placement of steel plate shear walls for steel frames by bat algorithm. Struct Des Tall Spec Build 24(1):1–18

    Article  Google Scholar 

  26. Gholizadeh S, Poorhoseini H (2015) Optimum design of steel frame structures by a modified dolphin echolocation algorithm. Struct Eng Mech 55(3):535–554

    Article  Google Scholar 

  27. Lopes S, Gomes GF, Mendez YAD, P da SL Alexandrino, 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. Arch Comput Methods Eng 4(1):1–15

    Google Scholar 

  28. Llc CRCP, Liu G-R, Han X (2003) Computational inverse techniques in nondestructive evaluation. CRC Press, Boca Raton

    Google Scholar 

  29. Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  30. Nobahari M, Seyedpoor SM (2013) An efficient method for structural damage localization based on the concepts of flexibility matrix and strain energy of a structure. Struct Eng Mech 46(2):231–244

    Article  Google Scholar 

  31. Torkzadeh P, Khamseh M (2014) Structural engineering a two-stage damage detection method for truss structures using FRF data and LMPSO algorithm. Iran J Struct Eng 1(2):114–125

    Google Scholar 

  32. Jiang S, Zhang C, Zhang S (2011) Two-stage structural damage detection using fuzzy neural networks and data fusion techniques. Expert Syst Appl 38(1):511–519

    Article  Google Scholar 

  33. Catbas N, Malekzadeh M, Gul M, Kwon I-B (2014) An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis. Smart Struct Syst 14(5):917

    Article  Google Scholar 

  34. Malekzadeh M, Atia G, Catbas FN (2015) Performance-based structural health monitoring through an innovative hybrid data interpretation framework. J Civ Struct Heal Monit 5(3):287–305

    Article  Google Scholar 

  35. Ghiasi R, Torkzadeh P, Noori M (2014) Structural damage detection using artificial neural networks and least square support vector machine with particle swarm harmony search algorithm. Int J Sustain Mater Struct Syst 1(4):303–320

    Google Scholar 

  36. Fathnejat H, Ghiasi R, Torkzadeh P (2016) Damage detection of plate-like structures using intelligent surrogate model. Smart Struct Syst 18(6):159–189

    Google Scholar 

  37. Matarazzo TJ, Pakzad SN (2016) Truncated physical model for dynamic sensor networks with applications in high-resolution mobile sensing and BIGDATA. J Eng Mech 142(5):1–13

    Google Scholar 

  38. Jia F, Lei Y, Lin J, Zhou X, Lu N (2016) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72:303–315

    Article  Google Scholar 

  39. Simpson A (1973) A generalization of Kron’s eigenvalue procedure. J Sound Vib 26:129–139

    Article  MATH  Google Scholar 

  40. Bathe JKJ, Wilson EL (1989) Numerical methods in finite element analysis. Prentice-Hall, Inc., Englewood Cliffs

    MATH  Google Scholar 

  41. Weng S, Xia Y, Xu Y-L, Zhou X-Q, Zhu H-P (Jun. 2009) Improved substructuring method for eigensolutions of large-scale structures. J Sound Vib 323(3–5):718–736

    Article  Google Scholar 

  42. Simpson A (1974) Scanning Kron’s determinant. Q J Mech Appl Mech 27:27–43

    Article  MathSciNet  MATH  Google Scholar 

  43. Messina A, Williams EJ, Contursi T (1998) Structural damage detection by a sensitivity and statistical-based method. J Sound Vib 216(5):791–808

    Article  Google Scholar 

  44. Guo HY, Li ZL (2009) A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm. Mech Syst Signal Process 23(3):769–782

    Article  Google Scholar 

  45. Paz M (1997) Structural dynamics: theory and computation. Springer, New York

    Book  Google Scholar 

  46. Seyedpoor SM (2012) A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization. Int J Non Linear Mech 47(1):1–8

    Article  MathSciNet  Google Scholar 

  47. Yang X-S, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  48. Komarasamy G, Wahi A (2012) An optimized K-means clustering technique using bat algorithm. Eur J Sci Res 84(2):263–273

    Google Scholar 

  49. Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math 2013:1–21

    MathSciNet  MATH  Google Scholar 

  50. Kennedy J (2010) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, New York, pp 760–766

    Google Scholar 

  51. He S, Wu QH, Wen JY, Saunders JR, Paton RC (2004) A particle swarm optimizer with passive congregation. Biosystems 78(1):135–147

    Article  Google Scholar 

  52. Parrish JK, Hamner WM (1997) Animal groups in three dimensions: how species aggregate. Cambridge University Press, Cambridge

    Book  Google Scholar 

  53. Release M (2012) The MathWorks. MathWorks. Inc., Natick

    Google Scholar 

  54. McKenna F, Fenves GL, Scott MH, Jeremic B (2000) Open System for earthquake engineering simulation (OpenSees). Pacific Earthquake Engineering Research Center. University of California, Berkeley

    Google Scholar 

  55. Gholizadeh S, Salajegheh E, Torkzadeh P (2008) Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network. J Sound Vib 312(1–2):316–331

    Article  Google Scholar 

  56. Koh BH, Dyke SJ (Feb. 2007) Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data. Comput Struct 85(3–4):117–130

    Article  Google Scholar 

  57. Salajegheh E, Gholizadeh S (2005) Optimum design of structures by an improved genetic algorithm using neural networks. Adv Eng Softw 36(11–12):757–767

    Article  Google Scholar 

  58. Lallemand B, Level P, Duveau H, Mahieux B (1999) Eigensolutions sensitivity analysis using a sub-structuring method. Comput Struct 71(3):257–265

    Article  Google Scholar 

  59. Cai X, Wang L, Kang Q, Wu Q (2014) Bat algorithm with Gaussian walk. Int J Bio-Inspired Comput 6(3):166–174

    Article  Google Scholar 

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Ghiasi, R., Fathnejat, H. & Torkzadeh, P. A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm. Engineering with Computers 35, 857–874 (2019). https://doi.org/10.1007/s00366-018-0636-0

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