[1]YU Chongchong,WU Zijun,TAN Li,et al.Unbalanced integrated transfer learning model and its application to bridge structural health monitoring[J].CAAI Transactions on Intelligent Systems,2013,8(1):46-51.[doi:10.3969/j.issn.1673-4785.201208002]
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Unbalanced integrated transfer learning model and its application to bridge structural health monitoring

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 1 Page number: 46-51 Column: 学术论文—机器学习 Public date: 2013-03-25
Title:
Unbalanced integrated transfer learning model and its application to bridge structural health monitoring
Author(s):
YU Chongchong1; 2 ;  WU Zijun 1;  TAN Li 1;  TU Xuyan 2;  TIAN Rui 1
1. School of Computer & Information Engineering, Beijing Technology and Business University, Beijing 100048, China;
2. School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Keywords:
unbalanced integrated transfer learning algorithm;  self organizing map algorithm;  transfer learning model;  bridge structural health monitoring
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201208002
Abstract:
The examination of bridge structural data obtained in the bridge structural health monitoring and condition assessment process had the problem of intermittent abnormalities or defects in the past. However, the classification of different samples of data is seen to be uneven, thus, making it difficult to complete structural health monitoring and condition assessment of the bridge under the condition of the absence of information and data distribution imbalance. In order to solve the problem mentioned above, this paper proposes an improved transfer learning model based on selforganizing map (SOM) clustering algorithm to improve the similarity measure function and unbalanced integration transfer learning algorithm. According to the analysis of actual monitoring data, the classification accuracy of the proposed transfer learning model increased as the increasing of the proportion of the target data set, validating the efficiency and scientificity of the proposed model.
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