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http://dx.doi.org/10.12989/eas.2016.11.5.841

A new method to identify bridge bearing damage based on Radial Basis Function Neural Network  

Chen, Zhaowei (State Key Laboratory of Traction Power, Southwest Jiaotong University)
Fang, Hui (Electric Power Research Institute, State Grid Chongqing Electric Power Co.)
Ke, Xinmeng (Locomotive Vehicle Department, Zhengzhou Railway Vocational and Technical College)
Zeng, Yiming (Locomotive and Car Research Institute, China Academy of Railway Sciences)
Publication Information
Earthquakes and Structures / v.11, no.5, 2016 , pp. 841-859 More about this Journal
Abstract
Bridge bearings are important connection elements between bridge superstructures and substructures, whose health states directly affect the performance of the bridges. This paper systematacially presents a new method to identify the bridge bearing damage based on the neural network theory. Firstly, based on the analysis of different damage types, a description of the bearing damage is introduced, and a uniform description for all the damage types is given. Then, the feasibility and sensitivity of identifying the bearing damage with bridge vibration modes are investigated. After that, a Radial Basis Function Neural Network (RBFNN) is built, whose input and output are the beam modal information and the damage information, respectively. Finally, trained by plenty of data samples formed by the numerical method, the network is employed to identify the bearing damage. Results show that the bridge bearing damage can be clearly reflected by the modal information of the bridge beam, which validates the effectiveness of the proposed method.
Keywords
bridge bearing; damage identification; vibration mode; Radial Basis Function Neural Network; finite element model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Gu, H.S. and Itoh, Y. (2010), "Ageing behaviour of natural rubber and high damping rubber materials used in bridge rubber bearings", Adv. Struct. Eng., 13(6), 1105-1113.   DOI
2 Hagan, M.T., Demuth, H.B., Beale, M.H. and De Jesus, O. (1996), Neural network design, PWS publishing company, Boston.
3 Hamzeh, O.N., Tassoulas, J.L. and Becker, E.B. (1998), "Behavior of elastomeric bridge bearings: computational results", J. Bridge Eng., ASCE, 3(3), 140-146.   DOI
4 He, X.H., Hua, X.G., Chen, Z.Q. and Huang, F.L. (2011), "EMD-based random decrement technique for modal parameter identification of an existing railway bridge", Eng. Struct., 33(4), 1348-1356.   DOI
5 Itoh, Y. and Gu, H.S. (2009), "Prediction of aging characteristics in natural rubber bearings used in bridges", J. Bridge Eng., ASCE, 14(2), 122-128.   DOI
6 Kim, S.H., Mha, H.S. and Lee, S.W. (2006), "Effect of bearing damage upon seismic behaviors of a multi-span girder bridge", Eng. Struct., 28(7), 1071-1080.   DOI
7 Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B. and Jung, H.Y. (2005), "Neural networks-based damage detection for bridges considering errors in baseline finite element models", J. Sound Vib., 280(3), 555-578.   DOI
8 Mutobe, R.M. and Cooper, T.R. (1999), "Nonlinear analysis of a large bridge with isolation bearings", Comput. Struct., 72(1), 279-292.   DOI
9 Olmos, B.A. and Roesset, J.M. (2010), "Effects of the nonlinear behavior of lead-rubber bearings on the seismic response of bridges", Earthq. Struct., 1(2), 215-230.   DOI
10 Tadesse, Z., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2012), "Neural networks for prediction of deflection in composite bridges", J. Constr. Steel Res., 68(1), 138-149.   DOI
11 Ubertini, F., Gentile, C. and Materazzi, A.L. (2013), "Automated modal identification in operational conditions and its application to bridges", Eng. Struct., 46, 264-278.   DOI
12 Yakut, A. and Yura, J.A. (2002), "Evaluation of low-temperature test methods for elastomeric bridge bearings", J. Bridge Eng., ASCE, 7(1), 50-56.   DOI
13 Zhuang, J.S. (2008), Bridge bearings, (3rd edition), China Railway Publishing House, Beijing. (in Chinese)
14 Clough, R.W. and Penzien, J. (2003), Dynamic of structures, (3rd edition), Computers & Structures Inc., Berkeley.
15 Ala, N., Power, E.H. and Azizinamini, A. (2015), "Predicting the service life of sliding surfaces in bridge bearings", J. Bridge Eng., ASCE, 21(2), 04015035.
16 Bakhary, N., Hao, H. and Deeks, A.J. (2007), "Damage detection using artificial neural network with consideration of uncertainties", Eng. Struct., 29(11), 2806-2815.   DOI
17 Chena, J., Xua, Y.L. and Zhang, R.C. (2004), "Modal parameter identification of Tsing Ma suspension bridge under Typhoon .Victor: EMD-HT method", J. Wind Eng. Ind. Aerod., 92(10), 805-827.   DOI
18 Domaneschi, M., Limongelli, M.P. and Martinelli, L. (2015), "Damage detection and localization on a benchmark cable-stayed bridge", Earthq. Struct., 8(5), 1113-1126.   DOI
19 Filipov, E.T., Fahnestock, L.A., Steelman, J.S., Hajjar, J.F., LaFave, J.M. and Foutch, D.A. (2013), "Evaluaion of quasi-isolated seismic bridge behavior using nonlinear bearing models", Eng. Struct., 49, 168-181.   DOI
20 Gilstad, D.E. (1990), "Bridge bearings and stability", J. Struct. Eng., ASCE, 116(5), 1269-1277.   DOI