Search

The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. UCS is crucial in assessing the stability and durability of rock masses, which directly influences the safety, efficiency, and cost-effectiveness of construction and excavation operations. Here's a refined version of your text for enhanced clarity and flow: in this part, the execution of the proposed model was compared for both single and hybrid configurations. Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). For training, 70% of the UCS dataset was utilized, while the remaining 30% was equally divided between testing (15%) and validation (15%). For the model evaluation, several metrics were considered in this work, including the R2, RMSE, WAPE, MAE, and RAE, which ensure fairness in the analysis. The closer the R2 value comes to 1, the better the performance. The error metrics should be close to 0 for better accuracy. From Table 2, one can observe that the result of the standalone SVR model gave an RMSE of 6.213 during training and 9.454 during testing, hence showing poor performance. However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. Among the hybrid models, the SVSH model had the best performance, with an R2 value of 0.998 and an RMSE of 1.261 during training. The SVCO model performed moderately, with an R2 value of 0.988 during training. [ABSTRACT FROM AUTHOR]

Abstract The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. UCS is crucial in assessing the stability and durability of rock masses, which directly influences the safety, efficiency, and cost-effectiveness of construction and excavation operations. Here’s a refined version of your text for enhanced clarity and flow: in this part, the execution of the proposed model was compared for both single and hybrid configurations. Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). For training, 70% of the UCS dataset was utilized, while the remaining 30% was equally divided between testing (15%) and validation (15%). For the model evaluation, several metrics were considered in this work, including the R 2, RMSE, WAPE, MAE, and RAE, which ensure fairness in the analysis. The closer the R 2 value comes to 1, the better the performance. The error metrics should be close to 0 for better accuracy. From Table 2, one can observe that the result of the standalone SVR model gave an RMSE of 6.213 during training and 9.454 during testing, hence showing poor performance. However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. Among the hybrid models, the SVSH model had the best performance, with an R 2 value of 0.998 and an RMSE of 1.261 during training. The SVCO model performed moderately, with an R 2 value of 0.988 during training.

Abstract It 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 (R 2) 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 R 2 and RMSE values for the KNIM model compared to KNN and KNAF is highly significant for the reliability and accuracy of the predictive model. R 2, 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 R 2 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.

The precise estimation of Unconfined Compressive Strength (UCS) in rock samples is paramount for the effective planning and execution of mining and civil engineering projects. However, the inherent variability and discontinuity within rock masses present challenges in obtaining accurate physico-mechanical data. In this study, the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) investigate as a cost-efficient and reliable approach to predict UCS from Bulk Density (BD), Bulk Tensile Strength (BTS), dry density (DD) test, p-wave velocity test (Vp), Schmidt hammer rebound number (SRn), and point load index test (Is(50)). ANFIS excels in effortlessly amalgamating expert knowledge using linguistic rules and empirical data, thus enhancing model interpretability and transparency, qualities profoundly esteemed in the geosciences. Furthermore, it allows for parameter fine-tuning optimizing model performance, as evidenced by its enhanced performance with the Mountain Gazelle Optimizer (MGO) and Adaptive Opposition Slime Mould Algorithm (AOSMA) in this study. Models were trained with 70% of the dataset, and the performance of the models was tested with 30% of the dataset. Performance indices such as R², RMSE, NMSE, MAE, and n_10 index were used to assess the predictive capability of models indicating that ANAS with maximum ÿ R²all=0.987 and minimum RMSEall=3.103, has the most optimal prediction performance for practical applications. [ABSTRACT FROM AUTHOR]