Developing Two Hybrid Algorithms for Predicting the Elastic Modulus of Intact Rocks

In the primary and final designs of projects related to rock mechanics engineering geology, one key parameters that needs be taken into account is intact elastic modulus (E). To measure this parameter in a laboratory setting, core samples with high-quality costly tools are required, which also makes for time-consuming process. The aim study assess effectiveness two meta-heuristic-driven approaches predicting E. models proposed paper, based on integrated expert systems, hybridize adaptive neuro-fuzzy inference system (ANFIS) optimization algorithms, i.e., differential evolution (DE) firefly algorithm (FA). performance quality both ANFIS-DE ANFIS-FA was then evaluated by comparing them ANFIS neural network (NN) models. were formed basis data collected from Azad Bakhtiari dam sites Iran. After applying several statistical criteria, such as root mean square error (RMSE), model found superior ANFIS-DE, ANFIS, NN terms E value. Additionally, sensitivity analysis results showed P-wave velocity further influenced compared other independent variables.