Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties

The dynamic compressive strength (DCS) of frozen-thawed rock influences the stability of rock mass in cold regions, especially when rock masses are possibly disturbed by dynamic loads. Laboratory freeze-thaw weathering treatment is usually time-consuming, and the dynamic strength test is destructive. Therefore, this paper attempts to quickly predict the DCS of frozen-thawed sandstones using data-driven methods, non-destructive rock properties, and basic environmental parameters. The sparrow search algorithm (SSA), gorilla troops optimiser, and dung beetle optimiser were chosen to develop two hyperparameters in the random forest (RF). The classic RF, back propagation neural network, and support vector regression models were taken as the control group. These six models were developed to predict the DCS. Their prediction results were compared. Finally, the sensitivity analysis was carried out to assess the significance of all input variables. The results indicate that the SSA – RF model yields the best prediction result, and three optimised models have better performance than single machine-learning models. Strain rate, dry density, and wave velocity are found to be the three most important parameters in DCS prediction, which further indicates that there is also a strong correlation between the characteristic impedance of the rock and the DCS.