Hakim, S.J.S. and Abdul Razak, H. (2011) Application of artificial neural network on vibration test data for damage identification in bridge girder. International Journal of the Physical Sciences, 6 (35). pp. 7991-8001. ISSN 1992-1950, DOI https://doi.org/10.5897/ijps11.1198.
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Abstract
Structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus, it is important to monitor structures for the occurrence, location and extent of damage. Artificial neural networks (ANNs) as a numerical technique have been applied increasingly for damage identification with varied success. ANNs are inspired by human biological neurons and have been used to model some specific problems in many areas of engineering and science to achieve reasonable results. ANNs have the ability to learn from examples and then adapt to changing situations when sufficient input-output data are available. This paper presents the application of ANNs for detection of damage in a steel girder bridge using natural frequencies as dynamic parameters. Dynamic parameters are easy to implement for damage assessment and can be directly linked to the topology of structure. In this study, the required data for the ANNs in the form of natural frequencies will be obtained from experimental modal analysis. This paper also highlights the concept of ANNs followed by the detail presentation of the experimental modal analysis for natural frequencies extraction. ©2011 Academic Journals.
| Item Type: | Article |
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| Funders: | UNSPECIFIED |
| Additional Information: | Cited By (since 1996):1 Export Date: 16 December 2013 Source: Scopus Language of Original Document: English Correspondence Address: Hakim, S. J. S.; Department of Civil Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia; email: jamalhakim@siswa.um.edu.my References: Abdul Razak, H., Choi, F.C., The Effect of corrosion on the natural frequency and modal damping of reinforced concrete beams (2001) J. Eng. Struct, 23 (1), pp. 1126-1133; Chakraborty, D., Artificial neural network based delamination prediction in laminated composites (2005) J. Mat. Design, 26, pp. 1-7; Chen, H.M., Tsai, K.H., Qi, G.Z., Yang, J.C.S., Amini, F., Neural network for structure control (1995) J. Comput. Civil Eng, 9 (2), pp. 168-175; Fonseca, E.T., Vellasco, P.G.S., A path load parametric analysis using neural networks (2003) J. Constructional Steel Res, 59, pp. 251-267; Ghaboussi, J., Joghatatie, A., Active control of structures using neural networks (1995) J. Eng. Mech., ASCE, 121 (4), pp. 55-567; Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M., Mohammadhassani, M., Application of Artificial Neural Networks to Predict Compressive strength of High Strength Concrete (2011) Int. J. Phys. Sci., (IJPS), 6 (5), pp. 975-981; Hola, J., Schabowicz, K., Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests (2005) J. Civil Eng. Manag, 11 (1), pp. 23-32; Ince, R., Prediction of fracture parameters of concrete by artificial neural networks (2004) J. Eng. Fracture Mech, 71, pp. 2143-2159; Jeyasehar, C.A., Sumangala, K., Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach (2006) J. Computers Struct, 84, pp. 1709-1718; Kim, Y.Y., Kapania, R.K., Neural networks for inverse problems in damage identification and optical imaging (2002) 43rd AIAA/ASME/ASCE/AHS/ASC Structures. Structural Dynamics and Materials Conferences, , Denver, Colorado; Kirkegaard, P., Rytter, A., Use of neural networks for damage assessment in a steel mast (1994) Proceedings of the 12th InternationalModal Analysis Conference, pp. 1128-1134; Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B., Jung, H.Y., Neural networks-based damage detection for bridges considering errors in baseline finite element models (2005) J. Sound, Vibration, 280 (3-5), pp. 555-578; Lee, S.C., Prediction of concrete strength using artificial neural networks (2003) J. Eng. Struct, 25, pp. 849-857; Mansour, M.Y., Dicleli, M., Lee, Y.L., Zhang, J., Predicting the shear strength of reinforced concrete beams using artificial neural networks (2004) J. Eng. Struct, 26, pp. 781-799; Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back propagation errors (1986) J. Nature, 323, pp. 533-536; Sahin, M., Shenoi, R.A., Quantification and localization of damage in beam-like structures by using artificial neural networks with experimental validation (2003) J. Eng. Struct, 25, pp. 1785-1802; Suh, M.W., Shim, M.B., Kim, M.Y., Crack identification using hybrid neuro-genetic technique (2000) J. Sound Vibration, 234 (4), pp. 617-635; Wu, Z.S., Xu, B., Yokoyama, K., Decentralized parametric damage based on neural networks (2002) J. Computer-Aided Civil Infrastruct. Eng, 17, pp. 175-184; Xu, B., Wu, Z.S., Yokoyama, K., A localized identification method with neural networks and its application to structural health monitoring (2002) Structural Engineering. JSCE, 48 A, pp. 419-427. , http://www.alyuda.com/index.html |
| Uncontrolled Keywords: | Artificial neural networks (ANNs), Back propagation (BP), Damage identification, Natural frequency |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Mr Jenal S |
| Date Deposited: | 24 Jan 2014 14:53 |
| Last Modified: | 20 Mar 2019 08:24 |
| URI: | http://eprints.um.edu.my/id/eprint/9051 |
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