Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

• Published in: Advances in Civil Engineering
• Main Authors: Cui, Fu-Qing, Zhang, Wei, Liu, Zhi-Yun, Wang, Wei, Chen, Jian-bing, Jin, Long, Peng, Hui
• Other Authors: Shen, Yanjun, National Natural Science Foundation of China, China Postdoctoral Science Foundation
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: permafrost
• Online Access: http://dx.doi.org/10.1155/2020/8898126; http://downloads.hindawi.com/journals/ace/2020/8898126.pdf; http://downloads.hindawi.com/journals/ace/2020/8898126.xml; https://onlinelibrary.wiley.com/doi/pdf/10.1155/2020/8898126

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai‐Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine‐grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine‐grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient ( R 2 ) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and ...