An approach for damage detection of space structures using combination of second order gradient and fuzzy logic methods

Document Type : Research Paper

Authors

1 Department of Civil Engineering, University of Hormozgan, Bandar Abbas, Iran

2 Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

This study presents a new method of damage detection using a combination of the second-order gradient Levenberg-Marquardt algorithm (SOGLMA) and fuzzy logic (FL) to solve the nonlinear damage detection equation for space frame structures. For damage detection in structures with a large number of degrees of freedom using the second-order gradient Levenberg-Marquardt algorithm, it is necessary to perform an iterative process of analysis and solving a set of simultaneous nonlinear equations that requires a lot of time. Therefore, the computation time and the number of iterations are reduced by using the proposed method "Combination of the second-order gradient Levenberg-Marquardt algorithm and fuzzy logic (SOGLMA-FL)". Acceleration response in sensor nodes obtained at different time steps from dynamic analysis are considered as input values for fuzzification. The output values of the proposed method after defuzzification are the damage extent of structural elements. The results show that the proposed damage detection method (SOGLMA-FL) has faster convergence, lower numbers of iteration and reduced computation time than the damage detection method (SOGLMA) for space frame structures.

Keywords


[1] D. K. Agarwalla, S. K. Abdul, S. K. Sahoo, Application of genetic fuzzy system for damage identification in cantilever beam structure, Procedia Engineering, 144 (2016), 215-225.
[2] J. S. Arora, Introduction to optimum design, McGraw-Hill New York, 1989.
[3] P. Beena, G. Ranjan, Structural damage detection using fuzzy cognitive maps and Hebbian learning, Applied Soft Computing Journal, 11(1) (2010), 1014-1020.
[4] M. Chandrashekhar, G. Ranjan, Damage assessment of structures with uncertainty by using mode-shape curvatures and fuzzy logic, Journal of Sound and Vibration, 326(3-5) (2009), 939-957.
[5] M. Chandrashekhar, G. Ranjan, Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation, Mechanical Systems and Signal Processing, 23 (2009), 384-404.
[6] D. Dinh-Cong, V. Ho-Huu, T. Vo-Duy, H. Q. Ngo-Thi, T. Nguyen-Thoi, Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function, Engineering Optimization, 50(8) (2018), 1233-1251.
[7] S. S. Edward, K. Powsiri, V. S. G. Hota, B. H. Udaya, Fuzzy logic expert system for automated damage detection from changes in strain energy mode shapes, Nondestructive Testing and Evaluation, 18(1) (2002), 1-20.
[8] M. M. Ettefagh, M. H. Sadeghi, S. Khanmohammadi, Structural damage detection using fuzzy classification and ARMA parametric modeling, Aerospace Mechanics Journal, 3(2) (2007), 85-98.
[9] S. J. S. Hakima, R. H. Abdul, Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification, Structural Engineering and Mechanics, 45(6) (2013), 779-802.
[10] S. S. Hameed, V. Muralidharan, B. K. Ane, Comparative analysis of fuzzy classifier and ANN with histogram features for defect detection and classification in planetary gearbox, Applied Soft Computing, 106 (2021). DOI:10.1016/j.asoc.2021.107306.
[11] S. S. Hameed, V. Muralidharan, D. P. Kumar, S. Ravikumar, Fault classification using fuzzy logic in an Epicyclic Gearbox with statistical features, SAE Technical Paper, (2021), 7 pages, DOI:10.4271/2021-28-0220.
[12] R. J. Hansen, A numerical method for solving Fredholm integral equations of the first kind using singular values, SIAM Journalon Numerical Analysis, 8(3) (1971), 616-622.
[13] E. Jahanfekr, M. R. Mohammadizadeh, S. Shojaee, Insight to damage identification in truss-type structures using a second-order gradient-based algorithm, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 45(4) (2021), 2145-2175.
[14] S. F. Jiang, C. M. Zhang, S. Zhang, Two-stage structural damage detection using fuzzy neural networks and data fusion techniques, Expert Systems with Applications, 38(1) (2011), 511-519.
[15] A. Kaveh, S. M. Javadi, M. Maniat, Damage assessment via modal data with a mixed particle swarm strategy, ray optimizer, and harmony search, Asian Journal of Civil Engineering, 15(1) (2014), 95-106.
[16] S. S. Kourehli, M. K. Chehre, A. G. Zamani, Prediction of structural damage location with adaptive neuro-fuzzy inferential system, Iranian Journal of Structural Engineering, 3 (2016), 61-71.
[17] D. P. Kumar, V. Muralidharan, S. Ravikumar, Histogram as features for fault detection of multi point cutting tool-A data driven approach, Applied Acoustics, 186 (2022). DOI:10.1016/j.apacoust.2021.
108456.
[18] X. Li, S. Law, Adaptive Tikhonov regularization for damage detection based on nonlinear model updating, Mechanical Systems and Signal Processing, 24(6) (2010), 1646-1664.
[19] D. W. Marquardt, An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11(2) (1963), 431-441.
[20] S. Mazzoni, F. Mc Kenna, M. H. Scott, G. L. Fenves, B. Jeremic, Open system for earthquake engineering simulation (OpenSees), University of California Berkeley USA, 2003.
[21] M. T. H. M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5(6) (1994), 989-993.
[22] A. Mojtahedi, M. A. Lotfollahi, Y. Hassanzadeha, M. M. Ettefagh, M. H. Aminfara, A. B. Aghdam, Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system, Applied Ocean Research, 33(4) (2011), 398-411.
[23] J. A. Momoh, X. W. Ma, K. Tomsovic, Overview and literature survey of fuzzy set theory in power system, IEEE Transactions on Power Systems, 10(3) (1995), 1676-1690.
[24] M. A. Oliveira, D. J. Inman, Performance analysis of simplified fuzzy ARTMAP and probabilistic neural networks for identifying structural damage growth, Applied Soft Computing Journal, 52(C) (2017), 53-63.
[25] D. R. Parhi, K. M. Manoj, S. Chinmaya, Prediction of cracks using FEA analysis and fuzzy logic approach, International Journal of Artificial Intelligence and Computational Research (I J A I C R), 4(1) (2012), 13-20.
[26] D. R. Parhi, C. Sasanka, Smart crack detection of a cracked cantilever beam using fuzzy logic technology with hybrid membership functions, Engineering and Technology Research, 3(8) (2011), 270-278.
[27] M. P. Prashant, G. Ranjan, Genetic fuzzy system for damage detection in beams and helicopter rotor blades, Computer Methods Applied Mechanics Engineering, 192(16-18) (2003), 2031-2057.
[28] G. Ranjan, A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data, Journal of Intelligent Material Systems and Structures, 12(6) (2001), 397-407.
[29] G. Ranjan, Fuzzy cognitive maps for structural damage detection, Fuzzy Cognitive Maps for Applied Sciences and Engineering, 54 (2014), 267-290.
[30] T. M. Reda, J. Lucero, Damage identification for structural health monitoring using fuzzy pattern recognition, Engineering Structures, 27(12) (2005), 1774-1783.
[31] S. Sahu, B. P. Kumar, D. R. Parhi, Intelligent hybrid fuzzy logic system for damage detection of beam-like structural elements, Journal of Theoretical and Applied, 55(2) (2017), 509-521.
[32] D. S. Samuel, D. J. Milton, L. J. Vicente, J. B. Michael, Structural damage detection by fuzzy clustering, Mechanical Systems and Signal Processing, 22(7) (2008), 1636-1649.
[33] J. P. Sawyer, S. S. Rao, Structural damage detection and identification using fuzzy logic, American Institute of Aeronautics and Astronautics Journal, 38(12) (2000), 2328-2335.
[34] R. Sethi, S. K. Senapati, D. R. Parhi, Structural damage detection by fuzzy logic technique, Applied Mechanics and Materials, 592-594 (2014), 1175-1179.
[35] A. Tarighat, Model based damage detection of concrete bridge deck using adaptive neuro-fuzzy inference system, International Journal of Civil Engineering, 11(3) (2013), 170-181.
[36] A. Teughels, J. Maeck, G. D. Roeck, Damage assessment by FE model updating using damage functions, Computers and Structures, 80(25) (2002), 1869-1879.
[37] A. Teughels, G. D. Roeck, Damage detection and parameter identification by finite element model updating, Archives of Computational Methods in Engineering, 12(2) (2005), 123-164.
[38] J. Wang, Q. S. Yang, Modified Tikhonov regularization in model updating for damage identification, Structural Engineering and Mechanics, 44(5) (2012), 585-600.
[39] B. M. Wilamowski, J. D. Irwin, Intelligent systems, Taylor and Francis Group, 2011.
[40] B. M. Wilamowski, H. Yu, Improved computation for Levenberg Marquardt training, IEEE Transactions on Neural Networks, 21(6) (2010), 930-937.
[41] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353.
[42] S. J. Zheng, Z. Q. Li, H. T. Wang, A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams, Expert Systems with Applications, 38(9) (2011), 11837-11842.