Determination of thermal damage in rock specimen using intelligent techniques

Studies conducted by several researchers suggest that a large variance exists in the morphological integrity of rocks when subjected to thermal treatment. The extent of thermal damage, D(T), can be quantified by analyzing the change in either the elastic modulus, ultrasonic wave velocities or the acoustic emission signals. However, these require the use of sophisticated laboratory equipment, which may not be readily available. Additionally, the shape and size of the sample has to adhere to the specifications that have been mandated for the corresponding experiments. This would further introduce delay in the process of assessing the damage. Therefore, in this study, new predictive models have been developed, which can predict the extent of damage (D(T)) from the physical properties of thermally-modified fine-grained Dholpur sandstone. The sandstone is a primary construction material, and has been widely used in several Indian monuments of historic and political importance. The models have been developed using statistical and soft-computing tools such as multivariate regression analysis (MVRA), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The physical properties viz. temperature (T), density (D), porosity (P), thermal expansion coefficients (EL and EV) and ultrasonic wave velocities (VP and VS), serve as predictor variables. The efficacy of the models has been tested by calculating the performance indices, namely, coefficient of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE) and variance account for (VAF). The results suggest that the ANFIS model has the best prediction capacity.