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

Abstract

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 (R2) 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 guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

“…. SVR was successfully applied in solving engineering problems including hydrocarbon reservoir prediction [62], the thermal conductivity of frozen soil prediction [63], and rock mass parameter [64] prediction. at algorithm is developed from the statistical learning theory, and the input variables were reflected into a high-dimensional space by using the kennel function [65,66].…”

Estimating the thermal conductivity of plutonic rocks from major oxide composition using machine learning

“…Recently, ML algorithms have been applied to estimate the thermal conductivity of soils (Cui et al 2020 ;Zhang et al 2020a ;Zhao et al 2022 ;Li et al 2022b ). Several studies have re vie wed the use of ML algorithms to predict soil thermal conductivity, including Zhao et al ( 2022 ) and Li et al (2022a , b ).…”

Prediction of thermal conductivity of underground tar-rich coal seam based on support vector machine

“…Although thermal conductivity of coal powder has been tested by experimental means under atmospheric pressure, there are relatively few studies on the determination of thermal conductivity of underground tar-rich coal seam. Due to the requirement of sophisticated test procedures, experimental measurement of thermal conductivity of underground tar-rich coal seam becomes impractical [6] . Fortunately, prediction models about thermal conductivity of many materials were proposed [7] .…”

“…Manoj [8] indicated that support vector machine model has the excellent prediction capability to predict the thermal conductivity of rocks using simple rock parameters. Cui et al [6] demonstrated that the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils. In fact, thermal conductivity of underground tarrich coal seam is influenced by numerous factors [9][10] .…”