DocumentCode :
3303530
Title :
SVM in Predicting the Deformation of Deep Foundation Pit in Soft Soil Area
Author :
Sun, Fuxue
Author_Institution :
Coll. of Archit. & Civil Eng., Wenzhou Univ., Wenzhou, China
fYear :
2010
fDate :
24-25 April 2010
Firstpage :
761
Lastpage :
763
Abstract :
Based on the measured deformation data series, future deformation value of deep foundation pit was predicted using Support Vector Machine (SVM) model in soft soil area. Gauss kernel function, Sequential minimal optimization arithmetic, and the parameter value of C andε were determined by testing. By using the method in example, results are shown to be in good agreement with measured data and laws reported in paper, and illustrates that SVM could perform well in solving fuzzy geotechnical engineering problem similar to deformation prediction. As another act, the method and conclusion can be considered as reference for colleagues.
Keywords :
Area measurement; Arithmetic; Deformable models; Gaussian processes; Kernel; Performance evaluation; Predictive models; Sequential analysis; Soil measurements; Support vector machines; deep foundation pit; deformation prediction; soft soil area; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on
Conference_Location :
Kaifeng, China
Print_ISBN :
978-1-4244-6595-8
Electronic_ISBN :
978-1-4244-6596-5
Type :
conf
DOI :
10.1109/MVHI.2010.167
Filename :
5532473
Link To Document :
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