Damage detection of steel bridge girder using Artificial Neural Networks

Hakim, S.J.S. and Abdul Razak, H. (2012) Damage detection of steel bridge girder using Artificial Neural Networks. In: 5th International Conference on Emerging Technologies in Non-Destructive Testing, NDT, 2012, Ioannina.

Full text not available from this repository. (Request a copy)
Official URL: http://www.scopus.com/inward/record.url?eid=2-s2.0...


Civil 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) are inspired by human biological neurons and have been applied dramatically for damage identification with varied success. The feasibility of ANNs as strong tool for predicting the severity of damage in a model steel girder bridge is examined in this research. Natural frequencies of a structure have strong effect on damage and are applied as effective input parameters to train the ANN in present study. The required data for the ANNs in the form of natural frequencies are obtained from experimental modal analysis. It has been shown that an ANN trained only with natural frequency data can determine the severity of damage with less than 5.6 error. The results seem to be quite promising as accurately as possible. © 2012 Taylor & Francis Group, London.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Conference code: 88391 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, Malaysia References: Razak, H.A., Choi, F.C., The effect of corrosion on the natural frequency and modal damping of reinforced concrete beams (2001) Engineering Structures, 23 (9), pp. 1126-1133. , DOI 10.1016/S0141-0296(01)00005-0, PII S0141029601000050; Chakraborty, D., Artificial neural network based delamination prediction in laminated composites (2005) Materials and Design, 26 (1), pp. 1-7. , DOI 10.1016/j.matdes.2004.04.008, PII S0261306904001104; Chen, H.M., Tsai, K.H., Qi, G.Z., Yang, J.C.S., Amini, F., Neural network for structure control (1995) Computing in Civil Engineering, 9 (2), pp. 168-175; Fang, J.Q., Jiao, G.Q., Implementation of BP-Network using frequency response function as networks input data (2007) Computer Simulation Journal, 3, pp. 87-95; Fonseca, E.T., Vellasco, P.G.S., A path load parametric analysis using neural networks (2003) Constructional Steel Research, 59, pp. 251-267; 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) International Journal of the Physical Sciences (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) Journal of Civil Engineering and Management, 11 (1), pp. 23-32; Ince, R., Prediction of fracture parameters of concrete by Artificial Neural Networks (2004) Engineering Fracture Mechanics, 71 (15), pp. 2143-2159. , DOI 10.1016/j.engfracmech.2003.12.004, PII S0013794403003370; 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) Sound and Vibration, 280 (3-5), pp. 555-578; Rosales, M.B., Filipich, C.P., Buezas, F.S., Crack detection in beam-like structures (2009) Engineering Structures, 31, pp. 2257-2264; Sahin, M., Shenoi, R.A., Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation (2003) Engineering Structures, 25 (14), pp. 1785-1802. , DOI 10.1016/j.engstruct.2003.08.001; Wu, Z., Xu, B., Yokoyama, K., Decentralized parametric damage detection based on neural networks (2002) Computer-Aided Civil and Infrastructure Engineering, 17 (3), pp. 175-184
Uncontrolled Keywords: Biological neuron, Civil structure, Damage Identification, Experimental modal analysis, Input parameter, Steel bridge girders, Steel girder bridge, Natural frequencies, Neural networks, Nondestructive examination, Plate girder bridges, Damage detection
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 27 Jan 2014 02:26
Last Modified: 27 Jan 2014 02:26
URI: http://eprints.um.edu.my/id/eprint/9052

Actions (login required)

View Item View Item