Predicting the most important geomechanical parameter of rock mass using the harmony search and teaching learning based optimization algorithms

Due to the difficulties in assessing the deformation of jointed aggregates at the laboratory scale, various in situ testing methods such as plate loading test and dilatometry can be used to consider the effect of scale and joints. Although these methods are currently the best, they are expensive, time consuming, and have operational difficulties during implementation. Therefore, in this paper, to overcome these problems, new harmony search algorithms (HS) and teaching-learning optimization algorithm (TLBO) are used to indirectly estimate the modulus of rock mass deformation. In these models, the rock mass classification score (RMR), uniaxial compressive strength of virgin rock (UCS), depth (D) and the modulus of elasticity of intact rock (Ei) as input parameters and the modulus of rock mass deformability (Em) as output parameter Used. In this paper, Using different statistical indicators, the model created by the algorithms is evaluated and validated. The evaluation results showed that the relationship accuracy for the harmonic search algorithm using R2 and VAF methods is about 0.91-0.93 and using the RMSE and MSE methods is between 0.000017-0.0042. Also, the relationship accuracy for the optimization algorithm Based on teaching and learning using R2 and VAF methods, about 0.92-0.95 and using RMSE and MSE methods were between 0.00001- 0.0032.