Abstract
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.
Highlights
The tensile strength (TS) of a rock is a critical variable for geotechnical, mining, and geological engineering applications in designing foundations, tunneling, ensuring slope stability, rock blasting, underground excavation, and mining [1,2,3,4]
The Brazilian tensile strength (BTS) test suggested by the International Society of Rock Mechanics is widely used, as it is a simple and easy-to-perform test [10]
After the data were collected, they were randomly split into training and test sets with an 80:20 ratio (80% training and 20% testing; [55]), and the ranges of the independent variables in the training data were normalized by subtracting their means and dividing them by their standard deviations
Summary
The TS of a rock is a critical variable for geotechnical, mining, and geological engineering applications in designing foundations, tunneling, ensuring slope stability, rock blasting, underground excavation, and mining [1,2,3,4]. Direct and indirect, are available for predicting the TS of rocks. Indirect methods are preferred because they are simple, economical, and faster at predicting the TS of rocks and reduce the burden on the laboratory facilities incurred by direct TS testing or limitations on laboratory facilities for direct TS testing. Various empirical relationships between the BTS and point load index (PLI), Shore hardness index, Schmidt hammer rebound number, ultrasonic pulse velocity, second cycle of slake durability tests (Id2 ), porosity (n), etc., are usually employed to estimate the BTS of different rock types, including evaporitic rocks [2,5,8,9,10,11,12,13,14]
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