E3S Web Conf.
Volume 205, 20202nd International Conference on Energy Geotechnics (ICEGT 2020)
|Number of page(s)||5|
|Section||Thermo-Hydro-Mechanical Properties of Geomaterials|
|Published online||18 November 2020|
Effective thermal conductivity of unsaturated soils based on deep learning algorithm
1 Geomechanics and Geotechnics, Kiel University, 24118 Kiel, Germany
2 Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
3 Department of Computer Engineering, Aligarh Muslim University, 202002 Aligarh, India
* Corresponding author: firstname.lastname@example.org
Soil thermal conductivity plays a critical role in the design of geo-structures and energy transportation systems. Effective thermal conductivity (ETC) of soil depends primarily on the degree of saturation, porosity and mineralogical composition. These controlling parameters have nonlinear dependencies, thus making prediction a nontrivial task. In this study, an artificial neural network (ANN) model is developed based on the deep learning (DL) algorithm to predict the effective thermal conductivity of unsaturated soil. A large dataset is constructed including porosity, degree of saturation and quartz content from literature to train and validate the developed model. The model is constructed with a different number of hidden layers and neurons in each hidden layer. The standard errors for training and testing are calculated for each variation of hidden layers and neurons. The network with the least error is adopted for prediction. Two sand types independent of training and validation data reported in the literature are considered for prediction of the ETC. Five simulation runs are performed for each sand, and the computed results are plotted against the reported experimental results. The results conclude that the developed ANN model provides an efficient, easy and straightforward way to predict soil thermal conductivity with reasonable accuracy.
Key words: Effective Thermal Conductivity / Artificial Neural Network / Deep Learning / Soft Computational Method
© The Authors, published by EDP Sciences, 2020
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