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|
Predicting the effective thermal conductivity of geo-materials using artificial neural networks
Marine and Land Geomechanics and Geotechnics, Kiel University, Kiel, Germany
* Corresponding author: firstname.lastname@example.org
Soil thermal conductivity is an important thermal property used in heat transfer modelling and geo-energy applications. Because of its complex nature and depending on several factors such as porosity, moister content, structure, etc., it is always challenging to predict the thermal conductivity of geo-materials. In the past, many predictions models like theoretical, semi-empirical, empirical models have been proposed based on the experimental data. However, these models are more specific to certain boundary conditions. Therefore, in this study, an artificial neural network (ANN) approach was used to predict the thermal conductivity of geo-materials as a function of porosity, gradation and mineralogy. A comparison between existing prediction models and the developed ANN model for predicting thermal conductivity is also given.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.