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.

The weakly cemented sandstone is widely distributed in the Western Mining Area, which is mainly formed by mineral grains and cemented minerals through compaction and cementation. To study the influence of grain size on the mechanical properties and acoustic emission (AE) characteristics of weakly cemented sandstone, uniaxial compression and Brazilian splitting AE tests were carried out on four weakly cemented sandstone specimens with different grain sizes. The physical properties, mechanical behaviors, and AE characteristics of sandstone under two conditions were analyzed, and the microfailure mechanism was investigated. The results show that the P-wave velocity, density, uniaxial compressive strength (UCS), and tensile strength of weakly cemented sandstones with different grain sizes decrease with the increase of grain size. The medium sandstone and coarse sandstone exhibit ductile failure, while the siltstone and fine sandstone exhibit brittle failure under the two conditions. The distribution of AE signal strength is nearly Gaussian in the time domain. The peak frequency and upper limit of signal strength are negatively correlated with grain size, and the occurrences of lots of high-strength AE signals can be used as the precursor of sandstone failure. The damage evolution shows the trend of low-speed damage-accelerated damage-low-speed damage, and the damage increase at the peak load is negatively related to the grain size. The microfailure mechanism is the tension-shear mixed failure, which is dominated by tensile failure, with few shear failures. The proportion of shear cracks is positively correlated with the grain size under uniaxial compression, while there is no significant correlation between shear cracks and grain size under Brazilian splitting.

The journal retracts the article titled “Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions”