Detection of Irregular Flaws in 2D Domains Using Extended Finite Element Method and Metaheuristics Algorithms, GA and GWO

Document Type : Original Research

Authors
A. fakheri kouzekanan 1
N. khaji 2
1 master student in structural engineering in tarbiat modares university
2 Professor of Civil Engineering, Graduate School of Advanced Science and Engineering, Hiroshima University, Japan
10.22034/23.6.9
Abstract
Today, one of the important issues in the industry is the failure of parts due to the presence of holes or cracks. Among the numerical calculation tools, the classical and extended finite element method is known as the most useful numerical tools in solving engineering science problems.

Identifying and investigating the types of cracks, flaws and cavities in structures is one of the most challenging issues in the field of engineering. In this article, the crack detection of two-dimensional (2D) structures using the extended finite element method (XFEM) along with genetic algorithm(GA) and grey wolf optimization method (GWO) to detect the existing crack and flaws by minimizing an error function which is also called as objective function that the evaluation of it, is based on difference between sensor measurements and suggested structure responses in each try of the algorithm. Damage detecting in 2D domains, as a non-destructive evaluation problem, is investigated using the extended finite element method along with the optimization method of genetic algorithm and grey wolf. The extended finite element method has been used to model the structure containing cracks and holes in the abaqus program, and genetic optimization and grey wolf method have been used to determine the location of the damage in which the codes were in matlab program.

The extended finite element method is a powerful tool for the analysis of structures containing cracks without remeshing and is therefore suitable for an iterative process in structural analysis. Also, in these problems, due to the wide range of parameters, it is not logical and rational to use mathematical methods. For this reason, meta heuristic methods have been developed, and grey wolf optimization methods and genetic algorithm are among these common non-gradient methods that are suitable for solving the inverse problem. This problem is set so that the optimizer algorithm finds the existing crack coordinates or holes coordinates by minimizing an objective function based on the values measured by the sensors installed on the structure. Among the limitations of the classical finite element method in the investigation of various problems in the field of fault and crack detection, we can point out the dependence of the crack or cavity on the finite element mesh, re-meshing and in other special cases the use of singular elements, which are completely removed by using The extended finite element. In this research, in order to identify the damage, the genetic optimization algorithm and the gray wolf have been used. These algorithms are designed in such a way to determine the characteristics of the damage by minimizing an error function. The defined error function is defined as the difference between the response obtained from the algorithm analysis and the response recorded in the main structure modeled in ABAQUS software, at the location of the sensors. Finally, three reference numerical examples have been solved to evaluate the capability and accuracy of the proposed method, and the result of the results shows a reduction in the cost of solving and an increase in the accuracy of the results.

Keywords

Subjects

[1] C. Ghee Koh, L. Ming See, T.Balendra, Damage detection of buildings: numerical and experimental studies, Journal of structural engineering, 121(8) (1995)1155-1160.
[2] Y.-L. Xu, J. He, Smart civil structures, CRC Press, 2017.
[3] J.P. Amezquita-Sanchez, H. Adeli, Signal processing techniques for vibration-based health monitoring of smart structures, Archives of Computational Methods in Engineering, 23(1)(2016) 1-15.
[4] L. Qiu, S. Yuan, C. Boller, An adaptive guided wave Gaussian mixture model for damage monitoring under time-varying conditions: Validation in a full-scale aircraft fatigue test, Structural health monitoring, 5(16) (2017)517-501.
[5] P. Liu, H.J. Lim, S. Yang, H. Sohn, C.H. Lee, Y. Yi, D. Kim, J. Jung, I.-h. Bae, Development of a “stick-and-detect” wireless sensor node for fatigue crack detection, Structural Health Monitoring, 2(16) (2017) 163-153.
[6] J. Xu, Z. Fu, Q. Han, G. Lacidogna, A. Carpinteri, Microcracking monitoring and fracture evaluation for crumb rubber concrete based on acoustic emission techniques, Structural Health Monitoring, 4(17) (2018) 958-946.
[7] D. Reagan, A. Sabato, C. Niezrecki, Feasibility of using digital image correlation for unmanned aerial vehicle structural health monitoring of bridges, Structural Health Monitoring, 5(17) (2018) 1072 1056.
[8] W.-H. Hu, S. Said, R.G. Rohrmann, Á. Cunha, J. Teng,Continuous dynamic monitoring of a prestressed concrete bridge based on strain, inclination and crack measurements over a 14-year span, Structural Health Monitoring, 5(17) (2018) 1094-1073.
[9] H. Kim, E. Ahn, M. Shin, S.-H. Sim, Crack and noncrack classification from concrete surface images using machine learning, Structural Health Monitoring, 3(18) (2019) -725 738.
[10] J. Valença, D. Dias-da-Costa, E. Júlio, H. Araújo, H. Costa,Automatic crack monitoring using photogrammetry and image processing, Measurement, 1(46) (2013) 441-433.
[11] D. Dias‐da‐Costa, J. Valença, E. Júlio, H. Araújo, Crack propagation monitoring using an image deformation approach, Structural Control and Health Monitoring,10(24) (2017) e1973.
[12] T. H.Yi, H. N. Li, M. Gu, Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge, Measurement,
1(46) (2013) 432-420
[13] A. Mohan, S. Poobal, Crack detection using image processing: A critical review and analysis, Alexandria Engineering Journal, 2(57) (2018) 798-787.
[14] Ostachowicz W, M, 2008 Damage detection of structures using spectral finite element method. Computers and Structures, 86 454–462.
[15] Khaji N. & Kazemi Noureini H. 2012 Detection of a through-thickness crack based on elastic wave scattering in plates, Part II: Inverse Solution. Asian Journal of Civil Engineering, 13 433–454.
[16] Yang Z. L., Liu G. R. & Lam K. Y. 2002 An inverse procedure for crack detection using integral strain measured by optical fibers. Smart Materials and Structures, 11 72–78.
[17] Rabinovich D., Givoli D. & Vigdergauz S. 2009 Crack identification by ‘arrival time’ using XFEM and a genetic algorithm. International Journal for Numerical Methods in Engineering, 77 337–359.
[18] Waisman H., Chatzi E. & Smyth A. W. 2010 Detection and quantification of flaws in structures by the extended finite element method and genetic algorithms.International Journal for Numerical Methods in Engineering, 82 303–328.
[19] Chatzi E. N., Hiriyur B., Waisman H. & Smyth A.W. Experimental application and enhancement of the XFEM-GA algorithm for the detection of flaws in structures. Computers and Structures, 89 556– 570,2011.
[20] P. da S. L. Alexandrino, G. F. Gomes, and S. S. Cunha, “A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making,” Inverse Probl. Sci. Eng., vol. 28, no. 1, pp. 21–46, Jan.2020.
[21] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46– 61, Mar. 2014.
[22] Livani, M.A., Khaji, N. & Zakian, P. Identification of multiple flaws in 2D structures using dynamic extended spectral finite element method with a universally enhanced meta-heuristic optimizer. Struct Multidisc Optim 57,(2018).
[23] Zhao, Wenhu & Du, Chengbin & Li, Xinzhu, “Flaw detection in concrete media using XFEM and an improved artificial fish swarm algorithm,”. IOP Conference Series: Materials Science and Engineering ,2020.
[24] Faisal Al Thobiani, Samir Khatir, Brahim Benaissa, Emad Ghandourah, Seyedali Mirjalili, Magd Abdel Wahab, A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification,Theoretical and Applied Fracture Mechanics,Volume 118,2022.
[25] Mohammadi S. 2002 Extended Finite Element Method. Wiley/Blackwell Publishing, the UK.
[26] Moran B., Sukumar N., Moes N. & Belytschko T.2000 Extended finite element method for three dimensional crack modelling. International Journal for Numerical Methods in Engineering, 48 1549– 1570.
[27] Dolbow J., Sukumar N., Daux C., Moes N. & Belytschko T. 2000 Arbitrary branched and intersecting cracks with the extended finite element method. International Journal for Numerical Methods in Engineering, 48 1741–1760.
[28] Kaveh A., Bakhshpoori T. & Afshari E. 2014 An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm. Computers and Structures, 143 40–45.
[29] Kaveh A. 2014 Advances in Metaheuristic Algorithms for Optimal Design of Structures. SpringerVerlag, Switzerland.
[30] S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer, Advances in engineering software, 2014.
Modares Civil Engineering journal
Volume 23, Issue 6
November and December 2023
Pages 131-142