Indirect Estimation of Uniaxial Compressive Strength of Rock Using New Meta-Heuristic Algorithms

The uniaxial compressive strength parameter is an important and key parameter in geomechanical engineering that is used in rock engineering classification, study of rock fracture criteria and in the design stage of many construction and mining projects. In many cases, due to the lack of access to quality cores, determining these parameters in the laboratory is associated with many difficulties, and often this parameter is indirectly estimated from regression relationships that these relationships are not very accurate. The aim of this study is to use the new meta-heuristic algorithms of gray wolf (GWO) and fruit fly algorithm (FFOA) to indirectly estimate uniaxial compressive strength. To achieve this goal, data from 124 granite rock samples from the Selangor freshwater transfer tunnel project in Malaysia, have been used. Finally, to evaluate and validate the models obtained by intelligent algorithms, the indices of statistical are used. According to the results obtained in this paper as well as the validation of the models, the predicted values ​​of uniaxial compressive strength are very close to the real values ​​of the region, indicating the low error of the models in indirect estimation. In addition, in this paper, sensitivity analysis was performed on the effective parameters in estimating uniaxial compressive strength. The results showed that the return values ​​of Schmidt hammer (Rn), among other input parameters, have the greatest effect on uniaxial compressive strength.