Full Text:  <980>

Summary:  <354>

CLC number: TU43

On-line Access: 2021-08-20

Received: 2020-09-08

Revision Accepted: 2020-12-06

Crosschecked: 2021-07-30

Cited: 0

Clicked: 1606

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Nguyen Tien Khiem

https://orcid.org/0000-0001-5195-2704

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A

Accepted manuscript available online (unedited version)


Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network


Author(s):  Nguyen Tien Khiem, Tran Van Lien, Ngo Trong Duc

Affiliation(s):  Institute of Mechanics, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam; more

Corresponding email(s):  LienTV@nuce.edu.vn

Key Words:  Crack identification; Functionally graded material (FGM); Neural network (NN); Stationary wavelet transform (SWT); Dynamic stiffness method


Share this article to? More <<< Previous Paper|Next Paper >>>

Nguyen Tien Khiem, Tran Van Lien, Ngo Trong Duc. Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network[J]. Journal of Zhejiang University Science A, 2021, 22(5): 657-671.

@article{title="Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network",
author="Nguyen Tien Khiem, Tran Van Lien, Ngo Trong Duc",
journal="Journal of Zhejiang University Science A",
volume="22",
number="8",
pages="657-671",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000402"
}

%0 Journal Article
%T Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network
%A Nguyen Tien Khiem
%A Tran Van Lien
%A Ngo Trong Duc
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 8
%P 657-671
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer

TY - JOUR
T1 - Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network
A1 - Nguyen Tien Khiem
A1 - Tran Van Lien
A1 - Ngo Trong Duc
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 8
SP - 657
EP - 671
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -


Abstract: 
In this paper, an integrated procedure is proposed to identify cracks in a portal framed structure made of functionally graded material (FGM) using stationary wavelet transform (SWT) and neural network (NN). Material properties of the structure vary along the thickness of beam elements by the power law of volumn distribution. Cracks are assumed to be open and are modeled by double massless springs with stiffness calculated from their depth. The dynamic stiffness method (DSM) is developed to calculate the mode shapes of a cracked frame structure based on shape functions obtained as a general solution of vibration in multiple cracked FGM Timoshenko beams. The SWT of mode shapes is examined for localization of potential cracks in the frame structure and utilized as the input data of NN for crack depth identification. The integrated procedure proposed is shown to be very effective for accurately assessing crack locations and depths in FGM structures, even with noisy measured mode shapes and a limited amount of measured data.

使用稳定小波转换和神经网络识别功能梯度材料框架结构裂纹

目的:功能梯度材料(FGM)框架结构的裂纹识别.
创新点:1. 可接收多裂纹FGM结构在任意高频带中的精确模态.2. 提出了一种使用稳定小波转换(SWT)模态和神经网络识别FGM框架结构裂纹的一体化程序.
方法:使用动态刚度方法并结合与频率相关的形状函数,填补有限元方法的空白.这些形状函数被认为是频域内振动问题的精确解.
结论:1. 神经网络与SWT模态振型方法相结合,即使在测得的模态噪声很大的情况下,也能准确评估FGM结构的裂纹深度.2. 本项研究中提出的FGM框架多裂纹识别一体化程序也适用于有限测量数据的情况,且这些数据不仅局限于模态,还包括结构的静态或动态挠度.

关键词组:裂纹识别;功能梯度材料;神经网络;平稳小波变换;动刚度法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abolbashari MH, Nazari F, Rad JS, 2014. A multi-crack effects analysis and crack identification in functionally graded beams using particle swarm optimization algorithm and artificial neural network. Structural Engineering and Mechanics, 51(2):299-313.

[2]Akbaş ŞD, 2013. Free vibration characteristics of edge cracked functionally graded beams by using finite element method. International Journal of Engineering Trends and Technology, 4(10):4590-4597.

[3]Anitescu C, Atroshchenko E, Alajlan N, et al., 2019. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 59(1):345-359.

[4]Aydin K, 2013. Free vibration of functionally graded beams with arbitrary number of surface cracks. European Journal of Mechanics-A/Solids, 42:112-124.

[5]Aydin K, Kisi O, 2015. Damage diagnosis in beam-like structures by artificial neural networks. Journal of Civil Engineering and Management, 21(5):591-604.

[6]Banerjee A, Panigrahi B, Pohit G, 2016. Crack modelling and detection in Timoshenko FGM beam under transverse vibration using frequency contour and response surface model with GA. Nondestructive Testing and Evaluation, 31(2):142-164.

[7]Banerjee JR, Ananthapuvirajah A, 2018. Free vibration of functionally graded beams and frameworks using the dynamic stiffness method. Journal of Sound and Vibration, 422:34-47.

[8]Caddemi S, Caliò I, 2013. The exact explicit dynamic stiffness matrix of multi-cracked Euler–Bernoulli beam and applications to damaged frame structures. Journal of Sound and Vibration, 332(12):3049-3063.

[9]Caddemi S, Caliò I, Cannizzaro F, et al., 2018. A procedure for the identification of multiple cracks on beams and frames by static measurements. Structural Control and Health Monitoring, 25(8):e2194.

[10]Chakraverty S, Pradhan KK, 2016. Vibration of Functionally Graded Beams and Plates. Academic Press, London, UK.

[11]Chang CC, Chen LW, 2005. Detection of the location and size of cracks in the multiple cracked beam by spatial wavelet based approach. Mechanical Systems and Signal Processing, 19(1):139-155.

[12]Deng XM, Wang Q, 1998. Crack detection using spatial measurements and wavelet analysis. International Journal of Fracture, 91(2):L23-L28.

[13]Douka E, Loutridis S, Trochidis A, 2003. Crack identification in beams using wavelet analysis. International Journal of Solids and Structures, 40(13-14):3557-3569.

[14]Eftekhari M, Eftekhari M, Hosseini M, 2013. Crack detection in functionally graded beams using conjugate gradient method. International Journal of Engineering-Transactions C: Aspects, 27(3):367-374.

[15]Elishakoff I, Pentaras D, Gentilini C, 2016. Mechanics of Functionally Graded Material Structures. World Scientific, Singapore.

[16]Eltaher MA, Alshorbagy AE, Mahmoud FF, 2013. Determination of neutral axis position and its effect on natural frequencies of functionally graded macro/ nanobeams. Composite Structures, 99:193-201.

[17]Erdogan F, Wu BH, 1997. The surface crack problem for a plate with functionally graded properties. Journal of Applied Mechanics, 64(3):449-456.

[18]Gu P, Asaro RJ, 1997. Cracks in functionally graded materials. International Journal of Solids and Structures, 34(1):1-17.

[19]Guo HW, Zhuang XY, Rabczuk T, 2019. A deep collocation method for the bending analysis of kirchhoff plate. Computers, Materials & Continua, 59(2):433-456.

[20]Hakim SJS, Razak HA, Ravanfar SA, 2016. Ensemble neural networks for structural damage identification using modal data. International Journal of Damage Mechanics, 25(3):400-430.

[21]Jin ZH, Batra RC, 1996. Some basic fracture mechanics concepts in functionally graded materials. Journal of the Mechanics and Physics of Solids, 44(8):1221-1235.

[22]Ke LL, Yang J, Kitipornchai S, et al., 2009. Flexural vibration and elastic buckling of a cracked Timoshenko beam made of functionally graded materials. Mechanics of Advanced Materials and Structures, 16(6):488-502.

[23]Khiem NT, 2006. Crack detection for structure based on the dynamic stiffness model and the inverse problem of vibration. Inverse Problems in Science and Engineering, 14(1):85-96.

[24]Khiem NT, Huyen NN, 2016. Uncoupled vibrations in functionally graded Timoshenko beam. Vietnam Journal of Science and Technology, 54(6):785-796.

[25]Khiem NT, Huyen NN, 2017. A method for crack identification in functionally graded Timoshenko beam. Nondestructive Testing and Evaluation, 32(3):319-341.

[26]Khiem NT, Huyen NN, Long NT, 2017. Vibration of cracked Timoshenko beam made of functionally graded material. In: Harvie JM, Baqersad J (Eds.), Shock & Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics & Optics, Volume 9. Springer, Cham, Germany, p.133-143.

[27]Kitipornchai S, Ke LL, Yang J, et al., 2009. Nonlinear vibration of edge cracked functionally graded Timoshenko beams. Journal of Sound and Vibration, 324(3-5):962-982.

[28]Larbi LO, Kaci A, Houari MSA, et al., 2013. An efficient shear deformation beam theory based on neutral surface position for bending and free vibration of functionally graded beams#. Mechanics Based Design of Structures and Machines, 41(4):421-433.

[29]Liew KM, Wang Q, 1998. Application of wavelet theory for crack identification in structures. Journal of Engineering Mechanics, 124(2):152-157.

[30]Lien VT, Duc NT, 2019. Crack identification in multiple cracked beams made of functionally graded material by using stationary wavelet transform of mode shapes. Vietnam Journal of Mechanics, 41(2):105-126.

[31]Lien VT, Duc NT, Hung DT, 2019a. Crack identification in FGM multi-span beams using neural network and stationary wavelet transform of mode shapes and dynamic deflections. Proceedings of the National Science Conference Engineering Mechanics (in Vietnamese).

[32]Lien VT, Duc NT, Khiem NT, 2019b. Free and forced vibration analysis of multiple cracked FGM multi span continuous beams using dynamic stiffness method. Latin American Journal of Solids and Structures, 16(2):e157.

[33]Liu SW, Huang JH, Sung JC, et al., 2002. Detection of cracks using neural networks and computational mechanics. Computer Methods in Applied Mechanics and Engineering, 191(25-26):2831-2845.

[34]Matbuly MS, Ragb O, Nassar M, 2009. Natural frequencies of a functionally graded cracked beam using the differential quadrature method. Applied Mathematics and Computation, 215(6):2307-2316.

[35]Mehrjoo M, Khaji N, Moharrami H, et al., 2008. Damage detection of truss bridge joints using Artificial Neural Networks. Expert Systems with Applications, 35(3):1122-1131.

[36]Nanthakumar SS, Lahmer T, Zhuang X, et al., 2016. Detection of material interfaces using a regularized level set method in piezoelectric structures. Inverse Problems in Science and Engineering, 24(1):153-176.

[37]Nazari F, Abolbashari MH, 2013. Double cracks identification in functionally graded beams using artificial neural network. Journal of Solid Mechanics, 5(1):14-21.

[38]Nematollahi MA, Farid M, Hematiyan MR, et al., 2012. Crack detection in beam-like structures using a wavelet-based neural network. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226(10):1243-1254.

[39]Ovanesova AV, Suarez LE, 2004. Applications of wavelet transforms to damage detection in frame structures. Engineering Structures, 26(1):39-49.

[40]Pan FY, Li WJ, Wang BL, et al., 2009. Viscoelastic fracture of multiple cracks in functionally graded materials. Computer Methods in Applied Mechanics and Engineering, 198(33-36):2643-2649.

[41]Panigrahi B, Pohit G, 2018. Study of non-linear dynamic behavior of open cracked functionally graded Timoshenko beam under forced excitation using harmonic balance method in conjunction with an iterative technique. Applied Mathematical Modelling, 57:248-267.

[42]Pesquet JC, Krim H, Carfantan H, 1996. Time-invariant orthonormal wavelet representations. IEEE Transactions on Signal Processing, 44(8):1964-1970.

[43]Quek ST, Wang Q, Zhang L, et al., 2001. Sensitivity analysis of crack detection in beams by wavelet technique. International Journal of Mechanical Sciences, 43(12):2899-2910.

[44]Rumelhart DE, McClelland JL, 1987. Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. MIT Press, USA.

[45]Samaniego E, Anitescu C, Goswami S, et al., 2020. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 362:112790.

[46]Sherafatnia K, Farrahi GH, Faghidian SA, 2013. Analytic approach to free vibration and buckling analysis of functionally graded beams with edge cracks using four engineering beam theories. International Journal of Engineering-Transactions C: Aspects, 27(6):979-990.

[47]Su H, Banerjee JR, 2015. Development of dynamic stiffness method for free vibration of functionally graded Timoshenko beams. Computers & Structures, 147:107-116.

[48]Wei D, Liu YH, Xiang ZH, 2012. An analytical method for free vibration analysis of functionally graded beams with edge cracks. Journal of Sound and Vibration, 331(7):1686-1700.

[49]Wu X, Ghaboussi J, Garrett Jr JH, 1992. Use of neural networks in detection of structural damage. Computers & Structures, 42(4):649-659.

[50]Yam LH, Yan YJ, Jiang JS, 2003. Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Composite Structures, 60(4):403-412.

[51]Yan T, Kitipornchai S, Yang J, et al., 2011. Dynamic behaviour of edge-cracked shear deformable functionally graded beams on an elastic foundation under a moving load. Composite Structures, 93(11):2992-3001.

[52]Yang J, Chen Y, 2008. Free vibration and buckling analyses of functionally graded beams with edge cracks. Composite Structures, 83(1):48-60.

[53]Yang J, Chen Y, Xiang Y, et al., 2008. Free and forced vibration of cracked inhomogeneous beams under an axial force and a moving load. Journal of Sound and Vibration, 312(1-2):166-181.

[54]Yu ZG, Chu FL, 2009. Identification of crack in functionally graded material beams using the p-version of finite element method. Journal of Sound and Vibration, 325(1-2):69-84.

[55]Zapico JL, González MP, Worden K, 2003. Damage assessment using neural networks. Mechanical Systems and Signal Processing, 17(1):119-125.

[56]Zhong SC, Oyadiji SO, 2007. Crack detection in simply supported beams without baseline modal parameters by stationary wavelet transform. Mechanical Systems and Signal Processing, 21(4):1853-1884.

[57]Zhu LF, Ke LL, Zhu XQ, et al., 2019. Crack identification of functionally graded beams using continuous wavelet transform. Composite Structures, 210:473-485.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE