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