###### Abstract
Their results showed that laboratory tests cannot be easily applied for the accurate prediction of rock TS. [PERSON] et al. [11] carried out a number of laboratory tests on a total of 87 granite-type samples in order to predict their BTS value. They only made use of simple and multiple regressions for this purpose due to the high expense and time requirement of laboratory tests. Their findings showed a higher capability of multiple regression models compared to simple regression ones in terms of the accuracy level in the BTS prediction. The literature also contains great efforts for the development of intelligent systems aiming to be applicable to approximating science and to provide a solution to problems that may arise in engineering contexts [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56]. Furthermore, a number of significant studies have been carried out with similar objectives with the present paper. Both artificial neural networks (ANNs) and statistical methods were employed by [PERSON] et al. [4] in order to predict TS in schistose rocks. They confirmed the higher accuracy level of ANNs compared to conventional methods regarding TS prediction. Likewise, ANNs are able to generalize results, while the conventional methods fail to do this. [PERSON] et al. [1] investigated the drawbacks of the artificial intelligent methods previously introduced in the literature, and for the first time applied various sets of genetic programming to experimental data aiming to predict limestone strength. In another project, a general prediction model was developed by [PERSON] et al. [57] in order to predict the values of UCS and BTS formed by rotary drilling into sedimentary rock. For the purpose of constructing and evaluating their proposed model, they utilized multiple regression analyses and prediction performance indices, respectively. They concluded that the model could be effectively applied to real-life situations. It is true that the ANN is a capable technique for solving/addressing engineering problems/issues; however, they are also subject to certain limitations, including low learning speed and an incapability to escape from local minima [58; 59]. To remove these disadvantages, ANN can be integrated with optimization algorithms, such as artificial bee colony (ABC), imperialist competitive algorithm (ICA), invasive weed optimization (IWO), and particle swarm optimization (PSO). In this manner, ANNs can perform in their prediction tasks with a higher efficiency through the adjustment of their bias and weight. In recent years, such hybrid systems have been applied to a number of geotechnical problems (see [60; 61; 62]). Based on the above discussion, the present paper attempts to accomplish the following objectives:  
1. To create an appropriate database applicable for the prediction of TS. 2. To develop a number of novel equations by means of simple regression analysis. 3. To design three hybrid intelligent models: IWO-ANN, ABC-ANN, and ICA-ANN. 4. To propose a hybrid intelligent model of the highest accuracy in predicting rock TS.