Browse > Article
http://dx.doi.org/10.12989/sss.2016.18.6.1233

Damage detection of plate-like structures using intelligent surrogate model  

Torkzadeh, Peyman (Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman)
Fathnejat, Hamed (Department of Civil Engineering, Graduate University of Advanced Technology)
Ghiasi, Ramin (Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman)
Publication Information
Smart Structures and Systems / v.18, no.6, 2016 , pp. 1233-1250 More about this Journal
Abstract
Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.
Keywords
damage detection; flexural plate structure; bat algorithm; curvature-moment derivative; optimized cascade feed-forward neural network;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Xiang, J., Matsumoto, T., Wang, Y. and Jiang, Z. (2013), "Detect damages in conical shells using curvature mode shape and wavelet finite element method", Int. J. Mech. Sci., 66, 83-93.   DOI
2 Yang, X.S. and Gandomi, A.H. (2012), "Bat algorithm: a novel approach for global engineering optimization", Eng. Comput., 29(5), 464-483.   DOI
3 Bai, Y., He, S., Nie, W., Gao, J. and Song, X. (2012), "Plane grid structure damage location identification by model curvature", Procedia Eng., 31, 534-540.   DOI
4 Ghodrati A.G., Seyed Razaghi, S.A. and Bagheri, A. (2011), "Damage detection in plates based on pattern search and genetic algorithms", Smart Struct. Syst., 7(2), 117-132.   DOI
5 Dessi, D. and Camerlengo, G. (2014), "Damage identification techniques via modal curvature analysis:overview and comparison", Mech. Syst. Signal Pr., 1-25.
6 Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: A review and comparative", Struct. Health Monit., 10(1), 83-111.   DOI
7 Fathnejat, H., Torkzadeh, P., Salajegheh, E. and 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.
8 Ghiasi, R., Ghasemi, M.R. and Noori, M. (2015), "Comparison of seven artificial intelligence methods for damage detection of structures", (Eds., Kruis, J., Tsompanakis, Y. and Topping, B.H.V.), Proceedings of the 15th International Conference on Civil, Structural and Environmental Engineering Computing, Civil-Comp Press, Stirlingshire, UK, Paper 116.
9 Ghiasi, R., Torkzadeh, P. and 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.   DOI
10 Gholizadeh, S. (2015), "Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network", Adv. Eng. Software, 81, 50-65.   DOI
11 Gholizadeh, S. and Shahrezaei, A.M. (2015), "Optimal placement of steel plate shear walls for steel frames by bat algorithm",Struct. Des. Tall Spec. Build., 24(1), 1-18.   DOI
12 Hagan, M.T. Demuth, H.B. and Beale, M.H. (1996), Neural Network Design, Boston.
13 Hera, A., Hou, Z. and Noori, M. (2013), "Wavelet-based techniques for structural health monitoring", Health Assessment of Engineered Structures, World Scientific, Ed. Achintya Haldar, 179-199.
14 Hakim, S.J.S. and Razak, H.A. (2014), "Modal parameters based structural damage detection using artificial neural networks-a review", Smart Struct. Syst., 14(2), 159-189.   DOI
15 He, W.Y. and Zhu, S. (2015), "Adaptive-scale damage detection strategy for plate structures based on wavelet finite element model", Struct. Eng. Mech., 54(2), 239-256.   DOI
16 Hedayat, A. Davilu, H. Barfrosh, A.A. and Sepanloo, K. (2009), "Estimation of research reactor core parameters using cascade feed forward artificial neural networks", Prog. Nucl. Energ., 51, 709-718.   DOI
17 Homaei, F., Shojaee, S. and Ghodrati A.G. (2014), "A direct damage detection method using multiple Damage localization index based on mode shapes criterion", Struct. Eng. Mech., 49(2), 183-202.   DOI
18 Hou, Z., Noori, M. and Amand, R.S. (2000), "Wavelet-based approach for structural damage detection", J. Eng. Mech. -ASCE, 126(7), 677-683.   DOI
19 Huang, X., Chen, J. and Zhu, H. (2016), "Assessing small failure probabilities by AK-SS: An active learning method combining kriging and subset simulation", Struct. Saf., 59, 86-95.   DOI
20 Iman, R. L. (2008), Latin Hypercube Sampling, John Wiley & Sons, Ltd.
21 Komarasamy, G. and Wahi, A. (2012), "An optimized K-means clustering technique using Bat algorithm", Eur. J. Sci. Res., 84(2), 263-273.
22 Loutridis, S., Douka, E., Hadjileontiadis, L.J. et al. (2005), "A two-dimensional wavelet transform for detection of cracks in plates", Eng. Struct., 27(9), 1327-1338.   DOI
23 Nobahari, M. and Seyedpoor, S.M. (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.   DOI
24 Makas, H. Yumusak, N. and Source, AD. (2013), "A comprehensive study on thyroid diagnosis by neural networks and swarm intelligence", Electronics, Computer and Computation (ICECCO).
25 Mazzoni, S., Mckenna, F., Michael, H.S. and Gregory, L.F. (2006), "OpenSees command language manual", Pacific Earthquake Engineering Research (PEER) Center.
26 Ng, A.Y. (2004), "Feature Selection, L 1 vs. L 2 Regularization, and Rotational Invariance", Proceedings of The Twenty-First International Conference on Machine learning, ACM.
27 Panchal, G., Ganatra, A., Kosta, Y. P. and Panchal, D. (2011), "Behavior analysis of multilayer perceptions with multiple hidden neurons and hidden layers", Int. J. Comput. Theory Eng., 3(2), 332-337.
28 Release, M. (2012), The MathWorks Inc., Natick, Massachusetts, United States.
29 Rucevskis, S., Sumbatyan, M.A., Akishin, P. and Chate, A. (2015), "Tikhonov's regularization approach in mode shape curvature analysis applied to damage detection", Mech. Res. Commun., 65, 9-16.   DOI
30 Sarvi, F., Shojaee, S. and Torkzadeh, P. (2014), "Damage identification of trusses by finite element model updating using an enhanced Levenberg-Marquardt algorithm", Int. J. Optim. Civil Eng., 4(2), 207-231.