%0 Journal Article
%T Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine
%A Bo Yan
%A Yao Cui
%A Lin Zhang
%A Chao Zhang
%A Yongzhi Yang
%A Zhenming Bao
%A Guobao Ning
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/850141
%X 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. 1. Introduction The designed life of an offshore platform is usually in 15~20 years. The maintenance cost of it is extremely expensive, but compared with its purchasing expense, it seems to be acceptable. As a result, from economic angle, it is important to evaluate the new platform, estimate residual life of existing platform, and prolong the life time of jacket platform for insuring production safety and improving production efficiency, extending lifespan and saving maintenance cost. Thus, it is necessary to provide an effective beam structure damage identification model to timely detect damage, evaluate damage degree, then verify and improve the design method of current platform, and provide references for future structure residual life assessment. There are many literatures about the damage identification problem. Kim and Melhem [1] summarized the applications of the wavelet analysis method in system damage checking and health monitoring in mechanical and other structures. Sun and Chang [2] utilized wavelet packet transform to analyze the signal of structure measurement; besides damage index based on wavelet packet is given and combined with neural network to identify the damage.
%U http://www.hindawi.com/journals/mpe/2014/850141/