The Forecasting for Artificial Frozen Soil Temperature Field Based on Support Vector Machines Method

Shi Yin Zhang; Jun Hao Chen; Fen Mao

doi:10.4028/www.scientific.net/AMM.193-194.995

Paper Titles

Research on Damage Identification of Suspension Bridge by Wavelet Analysis of the Strain Mode
p.976

Numerical Investigation of Stress Concentration Phenomenon in Piled Embankment under Moving Train Loads
p.980

Research on Applicability of Group Form Theory in Contemporary Urbanism: A Case Study of Hillside Terrace
p.984

Experimental Research on Shock Wave in Water of Underwater Drilling Blasting
p.989

The Forecasting for Artificial Frozen Soil Temperature Field Based on Support Vector Machines Method
p.995

The Damage Identification of Multilayer Frame Structure Containing Sheet Crack by Wavelet Analysis
p.1001

Technical and Economical Assessment of the Corrosion Preventive Methods for Maritime Reinforced Concrete Structures
p.1005

The Test Study to the Changes of Excess Pore Water Pressure during Precast Pile Construction
p.1010

An Introduction of the Case of AN Art and Craft Building Quality Judicatory Authentication
p.1014

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 193-194The Forecasting for Artificial Frozen Soil...

The Forecasting for Artificial Frozen Soil Temperature Field Based on Support Vector Machines Method

Abstract:

The level of soil freezing temperature has a direct impact on the time of formatting effective frozen wall and the value of freeze wall thickness, and it has a certain relationship with the soil composition, moisture content, density, liquid limit moisture content, pressure and saltness .For the feature of less measured sample of new mines, using the theory that is based on structural risk minimization and the method of small sample study-Support Vector Machine Algorithm, the support vector machine computation model of artificial frozen soil temperature has been established. Using support vector machines of different kernel functions to analyze the frozen temperature calculation of artificial freezing soil, the kernel function that is fit for the frozen temperature calculation of artificial has been determined. The results of support vector machine calculation model shows that this method is an effective method, it has provide a new approach for the frozen temperature calculation of artificial freezing soil.

You might also be interested in these eBooks

Frontiers of Green Building, Materials and Civil Engineering II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 193-194)

Pages:

995-1000

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.193-194.995

Citation:

Cite this paper

Online since:

August 2012

Authors:

Shi Yin Zhang, Jun Hao Chen, Fen Mao

Keywords:

Artificial Frozen Soil Temperature Field, Forecasting, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yao Zhaoming. The sensitivity analysis of factors affecting the temperature field development in artificial freezing soils. Hydrogeology & Engineering Geology, 2006, 33(3): 38-40. (in Chinese).

Google Scholar

[2] YAO Zhaoming, ZHOU Qijun. The intelligent method forecasting artificial frozen temperature field. Journal of Anhui University of Science and Technology (Natural Science). 2005, 25(3): 26-29.

Google Scholar

[3] Vapnik V N. Statistical Learning Theory[M]. New York: John and Wiley, (1998).

Google Scholar

[3] Platt J C. Sequential minimum optimization: A fast algorithm fortraining support machines[A]. In: Skolkpf B, Burges C J C, Smola AJ ed. Advanced in Kernel Methods-support Vector Learning[C]. Cambridge, MA: MIT Press, 1998. 185-208.

DOI: 10.7551/mitpress/1130.003.0016

Google Scholar

[4] Zhang Haoran, Han Zhengzhi, Li Changgang. Support vector machine based nonlinear systems identification[J]. Journal of system simulation: 2003, 15(1): 119-121. (in Chinese).

Google Scholar

[5] Zhao Hongbo, Feng Xiating. Study and application of genetic-support vector machine for nonlinear displacement time series forecasting[J]. Chinese Journal of Geotechnical Engineering, 2003, 22(10): 631-633. (in Chinese).

Google Scholar