###### Abstract  
In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design. invasive weed optimization; artificial neural network; hybrid model; Brazilian tensile strength +
Footnote †: journal: Applied Soft  
a laboratory [3]. On the other hand, in the indirect approach, the whole process can be made faster, simpler, and less costly by predicting the TS value with the help of other, less demanding laboratory experiments, e.g., the Schmidt hammer, P-wave velocity, density, and point load tests [1; 4; 5]. The list can be further elaborated with the Brazilian tensile strength (BTS) method, which has been proposed by the International Society for Rock Mechanics (ISRM) as an efficient method for determining TS [6]. Different researchers have suggested different empirical relations for the prediction of the TS value. Using statistical analyses, such as regression analysis and coefficients of variation, [PERSON] et al. [7] attempted to determine the rock features with key impacts on the penetration rate of percussive drills. The properties they marked out as significant were the point load strength, uniaxial compressive strength (UCS), BTS, and Schmidt hammer rebound number. In addition, aiming to predict the TS value, [PERSON] and [PERSON] [8] made use of two empirical methods: the block punch test and point load strength (Is\({}_{50}\)). Their findings indicated the superiority of the block punch index over Is\({}_{50}\) regarding the estimation of BTS. [PERSON] [9] reconfirmed the widely accepted idea that there is a correlation between BTS and UCS in rocks, and also indicated that the compressive strength of rock is roughly 10 times higher than the BTS level of the same rock. Nevertheless, rock has a site-specific behavior [PERSON] et al. [2] was mainly focused on how UCS and BTS are related to each other in various types of rock; the relative study resulted in finding a linear correlation between the two factors. [PERSON] et al. [3] compared all point load testing methods proposed in the literature in terms of their use in practical applications. They made use of the diametral, axial, and irregular methods for the purpose of predicting Is\({}_{50}\). A comparison was made on the obtained results, and a number of equations were developed in a way to both practically and economically estimate the BTS value. In addition, aiming to determine how directly TS and BTS are related to each other, [PERSON] and [PERSON] [10] analyzed rocks' TS. They also made a comprehensive review of the methods used to measure direct TS, BTS, and alternative methods for the estimation of a rock's TS.