Prediction of the uniaxial compressive strength of sandstone using various modeling techniques

Abstract

Sandstone blocks were collected from Dengkil site in Malaysia and brought to laboratory, and then intact samples prepared for testing. Rock tests, including Schmidt hammer rebound number, P-wave velocity, point load index, and UCS were conducted. The established dataset is composed of 108 cases. Consequently, the established dataset was utilized for developing the simple regression, linear, non-linear multiple regressions, artificial neural network, and a hybrid model, developed by integrating imperialist competitive algorithm with ANN. After performing the relevant models, several performance indices i.e. root mean squared error, coefficient of determination, variance account for, and total ranking, are examined for selecting the best model and comparing the obtained results. It is obtained that the ICA-ANN model is superior to the others. It is concluded that the hybrid of ICA-ANN could be used for predicting UCS of similar rock type in practice. © 2016 Elsevier Ltd.