DocumentCode :
1882200
Title :
The Prediction of Mechanical Properties of Cement Soil Based on PSO-SVM
Author :
Wang, Jiehao ; Xing, Yan ; Cheng, Liuyong ; Qin, Feihu ; Ma, Tianran
Author_Institution :
Sch. of Mech. & Civil Eng., China Univ. of Min. & Technol., Xuzhou, China
fYear :
2010
fDate :
10-12 Dec. 2010
Firstpage :
1
Lastpage :
4
Abstract :
The analysis of mechanical properties of cement soil is the problem concerned greatly by the design-constructors. This paper uses SVM to establish the nonlinear relation between the parameters and unconfined compressive strength of cement soil. At the same time, this paper uses PSO to get the global optimization of SVM, which avoids the blindness of artificial selection and improves the prediction accuracy of model. We apply PSO-SVM to the prediction of unconfined compressive strength, and then make a comparison with traditional BP-NN. The result shows that the prediction accuracy of PSO-SVM is much higher than BP-NN. Therefore, it is feasible and efficiency using PSO-SVM to predict the unconfined compressive strength of cement soil.
Keywords :
cements (building materials); compressive strength; mechanical engineering computing; particle swarm optimisation; soil; support vector machines; BP-NN; PSO-SVM; backpropagation neural network; cement soil mechanical properties; compressive strength; particle swarm optimisation; support vector machine; Accuracy; Artificial neural networks; Kernel; Mechanical factors; Soil; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
Type :
conf
DOI :
10.1109/CISE.2010.5677256
Filename :
5677256
Link To Document :
بازگشت