Uniaxial compressive strength (UCS) and the static Young’s modulus (E_s) are fundamental parameters for the effective design of engineering structures in a rock mass environment. Determining these two parameters in the laboratory is time-consuming and costly, and the results may be inappropriate if the testing process is not properly executed. Therefore, most researchers prefer alternative methods to estimate these two parameters. This work evaluates the thermal effect on the physical, chemical, and mechanical properties of marble rock, and proposes a prediction model for UCS and E_S using multi-linear regression (MLR), artificial neural networks (ANNs), random forest (RF), and k-nearest neighbor. The temperature (T), P-wave velocity (P_V), porosity (η), density (ρ), and dynamic Young’s modulus (E_d) were taken as input variables for the development of predictive models based on MLR, ANN, RF, and KNN. Moreover, the performance of the developed models was evaluated using the coefficient of determination (R²) and mean square error (MSE). The thermal effect results unveiled that, with increasing temperature, the UCS, E_S, P_V, and density decrease while the porosity increases. Furthermore, ES and UCS prediction models have an R² of 0.81 and 0.90 for MLR, respectively, and 0.85 and 0.95 for ANNs, respectively, while KNN and RF have given the R² value of 0.94 and 0.97 for both E_S and UCS. It is observed from the statistical analysis that P-waves and temperature show a strong correlation under the thermal effect in the prediction model of UCS and E_S. Based on predictive performance, the RF model is proposed as the best model for predicting UCS and E_S under thermal conditions.

Thermal treatment followed by subsequent cooling conditions (slow and rapid) can induce damage to the rock surface and internal structure, which may lead to the instability and failure of the rock. The extent of the damage is measured by the damage factor (D_T), which can be quantified in a laboratory by evaluating the changes in porosity, elastic modulus, ultrasonic velocities, acoustic emission signals, etc. However, the execution process for quantifying the damage factor necessitates laborious procedures and sophisticated equipment, which are time-consuming, costly, and may require technical expertise. Therefore, it is essential to quantify the extent of damage to the rock via alternate computer simulations. In this research, a new predictive model is proposed to quantify the damage factor. Three predictive models for quantifying the damage factors were developed based on multilinear regression (MLR), artificial neural networks (ANNs), and the adoptive neural-fuzzy inference system (ANFIS). The temperature (T), porosity (ρ), density (D), and P-waves were used as input variables in the development of predictive models for the damage factor. The performance of each predictive model was evaluated by the coefficient of determination (R²), the A20 index, the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the variance accounted for (VAF). The comparative analysis of predictive models revealed that ANN models used for predicting the rock damage factor based on porosity in slow conditions give an R² of 0.99, A20 index of 0.99, RMSE of 0.01, MAPE of 0.14, and a VAF of 100%, while rapid cooling gives an R² of 0.99, A20 index of 0.99, RMSE of 0.02, MAPE of 0.36%, and a VAF of 99.99%. It has been proposed that an ANN-based predictive model is the most efficient model for quantifying the rock damage factor based on porosity compared to other models. The findings of this study will facilitate the rapid quantification of damage factors induced by thermal treatment and cooling conditions for effective and successful engineering project execution in high-temperature rock mechanics environments.

This study investigated the effect of rock-water interaction on microstructural properties of various weathering grade sandstone. Sandstone samples were acquired from the Sor-Range coal mine area, Pakistan, and were investigated using a tabletop scanning electron microscope (SEM). The characteristics of microstructures from micrographs were obtained using ImageJ software. According to the findings an increase in wet and dry cycles significantly affects the microstructures (pore spaces and microcracks). The porosity and microcracks density of sandstone increases with the number of wet and dry cycles. Furthermore, the length of microcracks increases as the weathering grade increases. As variation in rock macroscopic mechanical characteristics is directly associated with the deterioration of microstructures. Therefore, analyzing the effect of water-rock interaction in various weathering grades of rock can offer a more accurate reference index for assessing the stability of geotechnical structures.