Tensile strength prediction of rock material using non-destructive tests: A comparative intelligent study

Abstract

Tensile strength of rock plays a significant role in the design of tunnels and underground engineering projects. Due to the inefficiency of direct method in determining rock tensile strength, the use of non-destructive tests has become a new direction in predicting the Brazilian Tensile Strength (BTS) of the rock samples. Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are three widely-used methods for BTS prediction. This study investigated the performance of these three intelligent models for BTS forecasting. In this regard, three non-destructive tests, namely Schmidt hammer, p-wave velocity, and density, were performed on 127 granitic rock samples, and their results were considered as input parameters. Then, the BTS tests were carried out on the samples and their results were considered as model output. Four measures of coefficient of determination (R2), Root mean square error (RMSE), Mean absolute error (MAE), and Scatter index (SI) were used for evaluation. The results showed that the ANFIS model, which is enjoying advantages of both ANN and FIS models, provides more accurate results in comparison with the proposed ANN and FIS models in predicting BTS values. R2 values for ANFIS, ANN, and FIS models were 0.92, 0.88, and 0.87, respectively. Besides, the ANFIS model could yield the lowest RMSE value of 81.5%, whereas RMSEs for FIS and ANN were 89.5% and 87.5%, respectively.