Prediction of thermal conductivity and damage in Indian Jalore granite for design of underground research laboratory

The measurement of thermal conductivity of granitic rocks is a time-consuming process and requires highly sophisticated instruments for experimental analysis. The difficulties in proposing reliable and precise empirical correlations and theoretical models forced the researchers to use alternative and more accurate models based on artificial intelligence (AI) techniques. The present study has been carried out to predict two parameters, thermal conductivity, and damage threshold for Jalore granite using AI techniques. In this study, artificial neural networks (ANNs), linear regression, support vector regressors (SVRs),, and decision tree regressors (DTRs) have been applied to obtain reliable and more accurate models. The extensive analysis revealed that the optimum performance is obtained by the ANN model with 8 nodes in the input layer with 2 hidden layers. Hidden layers having 15 and 7 nodes in the first and second layers, respectively. Softmax function has been applied as activation for each layer of the developed model. The mean absolute error (MAE) and mean squared error (MSE) values for the model while predicting the thermal coefficient were 0.0033671 and 184E−05. These values were 0.0016141 and 3.89E−06 for the damage threshold. DTRs with the number of estimators, n = 100, 1000, and 10,000 perform relatively well for predicting the values for both parameters. Linear regression and SVRs models (linear and robust kernel) show some deviations from the observed values at points where the trend shows a sudden change or kink. In the case of DTRs, the model with the number of estimators, n = 1000, and 100 performs most efficiently for predicting thermal conductivity (MSE = 6.81E−05) and the damage threshold (MSE = 2.84E−05), respectively.