Comparison of Six Machine-Learning Methods for Predicting the Tensile Strength (Brazilian) of Evaporitic Rocks

Featured Application Determination of rock tensile strength (TS) is an important task, especially during the initial design stage of engineering applications such as tunneling, slope stability, and foundation. Owing to its simplicity, the Brazilian tensile strength (BTS) test is widely used to assess the TS of rocks indirectly. Powerful regularization techniques such as the Elastic Net, Ridge, and Lasso; and Keras sequential models based on TensorFlow neural networks can be successfully used to predict BTS. Rock tensile strength (TS) is an important parameter for the initial design of engineering applications. The Brazilian tensile strength (BTS) test is suggested by the International Society of Rock Mechanics and the American Society for Testing Materials and is widely used to assess the TS of rocks indirectly. Evaporitic rock blocks were collected from Al Ain city in the United Arab Emirates. Samples were tested, and a database of 48 samples was created. Although previous studies have applied different methods such as adaptive neuro-fuzzy inference system and linear regression for BTS prediction, we are not aware of any study that employed regularization techniques, such as the Elastic Net, Ridge, and Lasso, and Keras based sequential neural network models. These techniques are powerful feature selection tools that can prevent overfitting to improve model performance and prediction accuracy. In this study, six algorithms, namely, the classical best subsets, three regularization techniques, and artificial neural networks with two application-programming interfaces (Keras on TensorFlow and Neural Net) were used to determine the best predictive model for the BTS. The models were compared through ten-fold cross-validation. The obtained results revealed that the model based on Keras on TensorFlow outperformed all the other considered models.