Unconfined compressive strength prediction of rock materials based on machine learning

Abstract

AbstractIt is costly, time-consuming, and difficult to measure unconfined compressive strength (UCS) using typical laboratory procedures, particularly when dealing with weak, extremely porous, and fractured rock. By efficiently choosing the variables from a subset of the dataset that includes the Schmidt hammer rebound number (SRn), bulk density (BD), bulk tensile strength (BTS), dry density (DD) test, p-wave velocity test (Vp), and point load index test (Is(50)), this study seeks to establish predictive models for the UCS of rocks. A prediction model for UCS was prepared using K-nearest neighbor (KNN). KNN was preferred over machine learning algorithms because it is simple, versatile, and interpretable. It is particularly useful when it has limited training time, faces non-parametric data with changing distributions, or requires straightforward explanations for predictions. In order to improve KNN’s prediction performance in this research, two optimization procedures (namely, Alibaba and the Forty Thieves (AFT) and Improved Manta-Ray Foraging Optimizer (IMRFO)) were used. Through comparison of KNN single modal performance with that of optimized versions, it is concluded that the KNIM (KNN model optimized with IMRFO) is an excellent possible applicant for the forecast of the UCS of rocks. This study’s results showed that the KNIM model is more suitable than the KNN single model and its counterpart KNAF in terms of accuracy as its correlation of determination (R2) values were 1.1% and 2% higher than KNN and KNAF and its root mean squared error (RMSE) values were 37.9% and 43.7% lower than KNN and KNAF. The improvement in R2 and RMSE values for the KNIM model compared to KNN and KNAF is highly significant for the reliability and accuracy of the predictive model. R2, measuring the proportion of variance predictable in the dependent variable (UCS of rocks) from the independent variables (model predictions), signifies a better fit to observed data. The elevated R2 values for KNIM indicate a stronger correlation with actual UCS values, enhancing the model’s accuracy in representing underlying patterns. Additionally, the reduction in RMSE values for KNIM implies that its predictions are, on average, closer to actual UCS values, contributing to a more accurate and reliable estimation of rock strength.