Browse > Article
http://dx.doi.org/10.12989/scs.2013.14.4.367

Structural damage detection of steel bridge girder using artificial neural networks and finite element models  

Hakim, S.J.S. (StrucHMRS Group, Department of Civil Engineering, University of Malaya)
Razak, H. Abdul (StrucHMRS Group, Department of Civil Engineering, University of Malaya)
Publication Information
Steel and Composite Structures / v.14, no.4, 2013 , pp. 367-377 More about this Journal
Abstract
Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.
Keywords
artificial neural networks (ANNs); finite element; damage detection; backpropagation algorithm; natural frequency;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Zapico, J.L., Gonzalez, M.P. Worden, K. (2003), "Damage assessment using neural networks", J. Mech. Sys. Signal Process., 17(1), 119-125.   DOI   ScienceOn
2 Matlab 7.11 (R2010b), The MathWorks Inc., MA, USA. http://www.tnodiana.com
3 Mohammadhassani, M., Nezam Abadi-Pour, H., Zamin Jumaat, M., Jameel, M. and Arumugam, A.M.S. (2013a), "Application of artificial neural network (ANN) and linear regressions (LR) in predicting the deflection of concrete deep beams", Computer and Concrete, 11(3). [In Print]
4 Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M., Jameel, M., Hakim, S.J.S. and Zargar, M. (2013b), "Application of the ANFIS model in deflection prediction of concrete deep beam", Struct. Eng. Mech., 45(3), 323-336.   DOI   ScienceOn
5 Nam, K.K., Haeng, L.S., Sup, J.K. (2009), "Prediction on the fatigue life of butt-welded specimens using artificial neural network", Steel Composite Struct., An Int'l J., 9(6), 557-568.   DOI   ScienceOn
6 Ni, Y.Q., Zhou, X.T. and Ko, J.M. (2006), "Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks", J. Sound Vib., 290(1-2), 242-263.   DOI   ScienceOn
7 Noorzaei, J., Hakim, S.J.S. and Jaafar, M.S. (2008), "An approach to predict ultimate bearing capacity of surface footings using artificial neural network", Indian Geotech. J., 38(4), 515-528.
8 Noorzaei, J., Hakim, S.J.S., Jaafar, M.S., Abang, A.A.A. and Thanoon, W.A.M. (2007), "An optimal architecture of artificial neural network for predicting of compressive strength of concrete", Indian Concrete J., 81(8), 17-24.
9 Park, J.H., Kim, J.T., Hong, D.S., Ho, D.D. and Yi, J.H. (2009), "Sequential damage detection approaches for beams using time-modal features and artificial neural networks", J. Sound Vib., 323, 451-474.   DOI   ScienceOn
10 Ramadas, C., Balasubramaniam, K., Joshi, M. and Krishnamurthy, C.V. (2008), "Detection of transverse cracks in a composite beam using combined features of lamb wave and vibration techniques in ANN environment", Int'l J. Smart Sens. Intellig. Sys., 1(10), 970-984.   DOI
11 Rosales, M.B., Filipich, C.P. and Buezas, F.S. (2009), "Crack detection in beam-like structures", Eng. Struct., 31(10), 2257-2264.   DOI   ScienceOn
12 Rytter, A. (1993), "Vibration based inspection of civil engineering structures", Ph.D. Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Denmark.
13 Stull, C.J. and Earls, C.J. (2009), "A rapid assessment methodology for bridges damage by truck strikes", Steel Composite Struct., An Int'l J., 9(3), 223-237.   DOI   ScienceOn
14 Suh, M.W., Shim, M.B. and Kim, M.Y. (2000), "Crack identification using hybrid neuro-genetic technique", J. Sound Vib., 234(4), 617-635.
15 Wu, Z.S., Xu, B. and Yokoyama, K (2002), "Decentralized parametric damage based on neural networks", J. Computer-Aided Civil Infrastruct. Eng., 17(3), 175-184.   DOI   ScienceOn
16 Xu, B., Wu, Z.S. and Yokoyama, K. (2002), "A localized identification method with neural networks and its application to structural health monitoring", J. Struct. Eng., JSCE, 48A, 419-427.
17 Yau, J.D. (2005), "Damage detection of a cracked column via a neural network approach", J. Adv. Steel Struct., 2, 1749-1754.
18 Fonseca, E.T. and Vellasco, P.G.S. (2003), "A path load parametric analysis using neural networks", J. Construct. Steel Res., 59, 251-267.   DOI   ScienceOn
19 Funahashi, K. (1989), "On the approximate realization of continuous mappings by neural networks", Neural Networks, 2(3), 183-192.   DOI   ScienceOn
20 Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996), Neural Network Design, PWS Publishing Company, Boston, MA, USA.
21 Kim, H., Cui, J., Seo, H.Y. and Lee, Y.H. (2009), "Iterative neural network strategy for static model identification of an FRP deck", Steel and Composite Struct., An Int'l J., 9(5), 445-455.   DOI   ScienceOn
22 Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M., Mohammadhassani, M. (2011), "Application of artificial neural networks to predict compressive strength of high strength concrete", Int'l J. Phys. Sci. (IJPS), 6(5), 975-981.
23 Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feed forward networks are universal approximator", Neural Networks., 2(5), 359-366.   DOI   ScienceOn
24 Inglessis, P., Medina, S., Lopez, A., Febres, R. and Lopez, J.F. (2002), "Modelling of local buckling in tubular steel frames by using plastic hinges with damage", Steel Composite Struct., An Int'l J., 2(1),57-65.
25 Lam, H.F. and Ng, C.T. (2008), "The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm", J. Eng. Struct., 30(10), 2762-2770.   DOI   ScienceOn
26 Lee, J.W., Kim, J.D., Yun, C.B., Yi, J.H. and Shim, M. (2002), "Health-monitoring method for bridges under ordinary traffic loadings", J. Sound Vib., 257(2), 247-264.   DOI   ScienceOn
27 Leu, S.S. and Loch, S. (2004), "Neural-network-based regression model of ground surface settlement induced by deep excavation", 13(3), 279-289.   DOI   ScienceOn
28 Lu, Y. and Tu, Z. (2004), "A two-level neural network approach for dynamic FE model updating including damping", J. Sound Vib., 275(3-5), 931-952.   DOI   ScienceOn
29 Mehrjoo, M., Khaji, N., Moharrami, H. and Bahreininejad, A. (2008), "Damage detection of truss bridge joints using artificial neural networks," J. Expert Sys. Appl., 35(3), 1122-1131.   DOI   ScienceOn