Sensitivity Analysis of Factors Affecting Frost Heave Characteristics of Coarse-grained Soil Subgrade Based on Optimized SVM

Jin-liang HE , Xiao-ning LI , Qing-long LI , Si-yuan ZHAO , Yang-jun DONG

Science Technology and Engineering ›› 2024, Vol. 24 ›› Issue (22) : 9577 -9586.

PDF
Science Technology and Engineering ›› 2024, Vol. 24 ›› Issue (22) : 9577-9586 DOI: 10.12404/j.issn.1671-1815.2305915
research-article
Sensitivity Analysis of Factors Affecting Frost Heave Characteristics of Coarse-grained Soil Subgrade Based on Optimized SVM
Author information +
History +
PDF

Abstract

In order to accurately understand the freezing and deformation characteristics of coarse-grained soil subgrade in cold regions based on indoor model tests, the effects of initial water content, freezing temperature, fine grains content, and number of freezing and thawing cycles on the freezing displacement and freezing rate were examined. Then, the SVM prediction model optimized by PSO-SVR was proposed to predict the freezing displacement value of coarse-grained soil subgrade. The sensitivity of each factor for the freezing expansion of the subgrade was determined by observing the variations of the output variable values through perturbing the input data of single-factor samples. The results indicate that the sensitivity of the studied factors influencing the freezing and expansion displacement of coarse-grained soil subgrade shows the following decreasing order: initial water content > freezing temperature > fine grains content, freezing and thawing cycles. The maximum freezing and expansion displacement of coarse-grained soil subgrade has a linear relationship with these four factors, where the freezing and expansion rate in rapid freezing period exhibit a positive proportional relationship with these four factors.

Cite this article

Download citation ▾
Jin-liang HE, Xiao-ning LI, Qing-long LI, Si-yuan ZHAO, Yang-jun DONG. Sensitivity Analysis of Factors Affecting Frost Heave Characteristics of Coarse-grained Soil Subgrade Based on Optimized SVM. Science Technology and Engineering, 2024, 24(22): 9577-9586 DOI:10.12404/j.issn.1671-1815.2305915

登录浏览全文

4963

注册一个新账户 忘记密码

References

Funding

PDF (2695KB)

15

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉