For studying the rapid and accurate classification method of pile foundation defects,a multi-layer classification method is adopted to improve one -to-one support vector machine(SVM)multiple classifiers structure,build one-to-one two-layer classification model and propose two-layer multiple classification method based on SVM theory.Compared with the BP neural network,the learning and training of two-layer SVM classification is faster and the real-time of the classification processing is better.In addition,it has stronger adaptability in test environment based on small sample and better classification accuracy.The method is fit for analyzing the multi-category classification problem of defects diagnose of pile foundation such as smaller training samples,higher precise and more input and output classification variables,and provides important support to the recognition of pile foundation which had several defects.
Key words
pile foundations /
support vector machine(SVM) /
multi2classification /
defects diagnose /
neural network
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References
[1] 张宏.灌注桩检测与处理.[M].北京:人民交通出版社,2001.1-12.
[2] 康维新.基于小波分析的桩基础完整性的检测[J].电子测量与仪器学报,2002.16(2):23-25.Kang Wei-xin.The test of pile foundation integrity based on wavelet analysis[J].Journal of Electronic Measurement and Instrument,2002,16(2):23-25.(in Chinese)
[3] kangweixin,pengxiyuan.Wavelet characteristic and experimental study of pile foundation fracture[J].7th International Conference on Electronic Measurement & Instruments (ICEMI).2005,6:302-305.
[4] 康维新,彭喜元.桩基小波特征的研究[J].电子测量与仪器学报,2005,19(5):25-27.Kang Wei-xin,Peng Xi-yuan.Pile foundation wavelet characteristic[J].Journal of Electronic Measurement and Instrument,2005.19(5):25-27.(in Chinese)
[5] V N Vapnik.An overview of statistical learning theory[J].IEEE Trans on Neural Networks,1999,10(5):988-999.
[6] Cortes C,Vapnik V.Support vector networks[J].Machine learning,1995,20:273-297.
[7] Scholkopf B,Sung K,Burges C,Girosi F,Niyogi P,Pogio T,Vapnik V.Comparing Support Vector Machines with Ganssian Kernels to Radial Basis Function Classifiers[R].A.I.Mem 01559,MIT,1996.
[8] 张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,(26)1:32-42.Zhang Xue-gong.Introduction to statistical learning theory andsupport vector machines[J].Acta Automatica Sinica,2000,26(1):32-42.(in Chinese)
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