Using different intelligent methods in Orange software to estimate the deformation modulus of rock mass

The deformation modulus indicates the degree of deformability of the rock mass in response to any loading and unloading, and it is important because it plays a role in the design of most underground structures. Estimation of this parameter on site is usually done with the help of two tests of plate loading and dilatometer, which is associated with spending a lot of money and time. In addition, due to the presence of discontinuities and cracks in the rock mass, laboratory tests on core samples also face errors. Today, to define a relationship between a parameter and its dependent parameters and to build a model to estimate or predict the chosen parameter, a variety of computational intelligence methods are used, and of course, they also provide favorable results. The purpose of this research is to use these types of algorithms in order to create an efficient model for predicting the deformation modulus on a database. In this regard, the performance of three models created by artificial neural network, K-nearest neighbor and random forest methods have been evaluated with the Orange software. The results showed that the artificial neural network model has the best performance and accuracy with RMSE=0.116 and MAE=0.094. Also, the sensitivity analysis of the input parameters shows that the RMR system is considered as an important and effective parameter.