Prediction of thermal conductivity of soils based on artificial intelligence algorithm

The thermal conductivity is the basic geotechnical thermodynamic parameter, and it is also one of the important parameters in the design of thermal geotechnical structures. It is affected by the factors such as soil skeleton mineral composition, and degree of saturation. The influencing factors for the thermal conductivity of soils are analyzed. Various advanced artificial intelligence algorithms are used to investigate the heat transfer mechanism of soils, and to establish prediction models. The correlation coefficient (R²), root mean square error (RMSE), mean absolute error (MAE) and variance ratio (VAF) are calculated to analyze the prediction error and robustness of these prediction models. By comparing withthe traditional empirical relationship model, the results show that the artificial neural network (ANN) model, the adaptive neural network-based fuzzy inference system (ANFIS) model and the support vector machine (SVM) model all predict the thermal conductivity of soils acceptably. The R² is greater than 0.9, the RMSE is less than 0.2 (W·m^-1·K^-1), the MAE is less than 0.13 (W·m^-1·K^-1), and the VAF is greater than 88% for all these prediction models. The accuracy of the proposed prediction models is significantly higher than that of the traditional empirical relationship model. According to the results of error statistics and robustness analysis, the ANN and SVM models are recommended in the prediction of the thermal conductivity of soils.