###### 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. e 3 - June 2021  
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Footnote †: email: +971-50-112-1905 (H.A.) hammer rebound number, ultrasonic pulse velocity, second cycle of slake durability tests (I\({}_{\text{d2}}\)), 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]. However, there are few direct studies on the BTS estimation of evaporitic rocks. [PERSON] et al. [12] collected 40 evaporitic rock blocks of the Early Miocene Gachsaran Formation from various locations in Iran. They prepared disk-shaped specimens from cylindrical core samples, which were drilled from evaporitic rock blocks for the BTS test, and the test specimens were tested under air-dry and saturated conditions. They reported that a strong correlation existed between BTS and PLI, but the saturated state (R\({}^{2}\) = 0.77) exhibited a weaker correlation compared to the air-dry state (R\({}^{2}\) = 0.87). In particular, they stressed that the provided empirical equations could be used only for evaporitic rocks of the Gachsaran Formation and not for evaporitic rocks in other areas. [PERSON] et al. [2] performed detailed studies on the strength estimation of evaporitic rocks from the Early Miocene Lower Fars (Gachsaran) Formation from Al Ain city in the United Arab Emirates (UAE). They prepared over 210 core specimens from the 48 evaporitic rock blocks collected from south of the Jabal Hafit mountain in Al Ain city and used some of these samples to prepare BTS disk specimens. BTS tests were conducted on 124 test samples as per the American Society for Testing and Materials (ASTM) standards [15]. They estimated the correlation coefficient, i.e., R-values between BTS and PLI and between BTS and I\({}_{\text{d2}}\) tests as 0.53 and 0.59, respectively. They highlighted that the textural and compositional characteristics of evaporitic rocks considerably affect their strength and recommended caution when dealing with evaporitic rocks in engineering applications owing to the effect of textural variations in test samples on the strength values. As alternatives to empirical modeling, various intelligent methods, such as artificial neural networks (ANNS), firefly algorithm, and neuro-fuzzy interface system (ANFIS), have been widely accepted in geoengineering disciplines. These methods are used for predicting rock properties (e.g., strength, elasticity modulus, and slope stability) and are applied in tasks such as tunneling and blasting [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. However, there are no studies on the use of alternative intelligent methods for predicting the TS of evaporitic rocks.