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H. Furuta¹, Y. Nomura², K. Nakatsu³, H. Hattori⁴ and S. Yasuda⁵

¹Faculty of Informatics, Kansai University, Japan
²Department of Science and Engineering, Ritsumeikan University, Japan
³Department of Modern Life, Osaka Jonan Women's Junior College, Japan
⁴Graduate School of Engineering, Kyoto University, Japan
⁵Graduate School of Informatics, Kansai University, Japan

doi:10.4203/ccp.103.13

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H. Furuta, Y. Nomura, K. Nakatsu, H. Hattori, S. Yasuda, "Influence of Exceptional Data on Bridge Damage Assessment based on One-Class SVM Pattern Recognition", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 13, 2013. doi:10.4203/ccp.103.13

Keywords: one-class support vector machine, neural network, pattern recognition.

Summary

Recently, it has become difficult to ensure the skill level of bridge maintenance engineers as a result of the retirement of experienced engineers. Hence, the daily inspection of bridges cannot be performed accurately and efficiently for the early detection of damage and to ensure appropriate measures. In previous research, damage assessment by introducing the technology of pattern recognition of digital images has been proposed. However, in the previous research, it was confirmed that there were some images which might be considered as exceptional data among the images of cracks of concrete decks. This paper describes the development of a support system for decision making in the damage assessment of bridges based on the pattern recognition by using the exception extraction. In this system, the exception extraction with one-class SVM removes some data that may cause the decrease of generalization capability of pattern recognition from the database. Thus, it may be expected that the accuracy and confidence of the results are improved. Numerical examples applying digital images of reinforced-concrete slabs of bridges with cracks are presented to demonstrate the practical use of the approach of this approach.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 103 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis Paper 13 Influence of Exceptional Data on Bridge Damage Assessment based on One-Class SVM Pattern Recognition H. Furuta¹, Y. Nomura², K. Nakatsu³, H. Hattori⁴ and S. Yasuda⁵ ¹Faculty of Informatics, Kansai University, Japan ²Department of Science and Engineering, Ritsumeikan University, Japan ³Department of Modern Life, Osaka Jonan Women's Junior College, Japan ⁴Graduate School of Engineering, Kyoto University, Japan ⁵Graduate School of Informatics, Kansai University, Japan doi:10.4203/ccp.103.13 purchase the full-text of this paper Full Bibliographic Reference for this paper H. Furuta, Y. Nomura, K. Nakatsu, H. Hattori, S. Yasuda, "Influence of Exceptional Data on Bridge Damage Assessment based on One-Class SVM Pattern Recognition", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 13, 2013. doi:10.4203/ccp.103.13 Keywords: one-class support vector machine, neural network, pattern recognition. Summary Recently, it has become difficult to ensure the skill level of bridge maintenance engineers as a result of the retirement of experienced engineers. Hence, the daily inspection of bridges cannot be performed accurately and efficiently for the early detection of damage and to ensure appropriate measures. In previous research, damage assessment by introducing the technology of pattern recognition of digital images has been proposed. However, in the previous research, it was confirmed that there were some images which might be considered as exceptional data among the images of cracks of concrete decks. This paper describes the development of a support system for decision making in the damage assessment of bridges based on the pattern recognition by using the exception extraction. In this system, the exception extraction with one-class SVM removes some data that may cause the decrease of generalization capability of pattern recognition from the database. Thus, it may be expected that the accuracy and confidence of the results are improved. Numerical examples applying digital images of reinforced-concrete slabs of bridges with cracks are presented to demonstrate the practical use of the approach of this approach. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £42 +P&P)
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