Rock Strength Estimation Using Several Tree-Based ML Techniques

The uniaxial compressive strength (UCS) of rock is an essential property of rock material in different relevant applications, such as rock slope, tunnel construction, and foundation. It takes enormous time and effort to obtain the UCS values directly in the laboratory. Accordingly, an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance. This study presents powerful boosting trees evaluation framework, i.e., adaptive boosting machine, extreme gradient boosting machine (XGBoost), and category gradient boosting machine, for estimating the UCS of sandstone. Schmidt hammer rebound number, P-wave velocity, and point load index were chosen as considered factors to forecast UCS values of sandstone samples. Taylor diagrams and five regression metrics, including coefficient of determination (R2), root mean square error, mean absolute error, variance account for, and A-20 index, were used to evaluate and compare the performance of these boosting trees. The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database. In particular, it was worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R2 and 0.958 testing R2. The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases. The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone.