Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

Bo Yan, Yao Cui, Lin Zhang, Chao Zhang, Yongzhi Yang, Zhenming Bao and Guobao Ning

Mathematical Problems in Engineering, 2014, vol. 2014, 1-8

Abstract:

It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:850141

DOI: 10.1155/2014/850141

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