Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment


Article Preview

BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.



Edited by:

Giorgio Monti and Enzo Martinelli




Y. H. Zhang, "Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment", Applied Mechanics and Materials, Vol. 847, pp. 440-444, 2016

Online since:

July 2016





* - Corresponding Author

[1] Z.R. Yuan, Artificial Neural Network and Application, Beijing: Tsinghua University press, 1996, pp.1-6.

[2] L.H. Yam, Theoretical and Experimental Study of Modal Strain Analysis, Journal of Sound and Vibration, vol. 191, No. 2, 1996, pp.251-260.

[3] D.R. Du, D.L. Chou, A.Q. Li, Application of Neural Network Technology in Civil Structural Health Monitoring, Nondestructive Measurement, vol. 26, No. 8, 2004, pp.383-387.

[4] C. Dong, Reliability theory and application of modern structural systems, Beijing: Science Press, 2001, pp.5-20.

[5] J.L. Chen, Y.L. Guo, Study on Damage Detection Based on Neural Network, Journal of Yantai University, vol. 14, No. 3, 2001, pp.217-223.

[6] Y.G. Luo, C.Z. Chen, Z.G. Wang, Neural Network Analysis of Beam Damage Diagnosis, Industrial Building, vol. 32, No. 1, 2002, pp.55-56.

[7] G.Y. Luo, H.J. Liu, Z.K. Wang, Study on Influence of Dynamic Characteristics of Steel Structure Damage, Journal of Shenyang University of Technology, vol. 24, No. 3, 2002, pp.248-248.