###### Abstract  
In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as traverting, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained. t  
experimental correlations to indirectly predict the UCS in a more economical way in terms of time and money, thanks to the ultrasonic pulse test [6]. Likewise, rocks are heterogeneous and anisotropic, so an equation that relates UCS with \(v_{p}\), \(\rho\) and \(n\) in the same equation, should be more reliable than with each variable separately (95% confidence interval and \(p\)-value \(<0.05\)), because, for the same type of rock, there may be variations of these index properties according to its location, as demonstrated by [PERSON] et al. [18] with saturated limestone. Another aspect to take into account in the determination of the UCS is the calculation method: (i) the Suggested Methods of the ISRM [5] only indicates that the value of each specimen is reported, while \"the number of specimens tested should be determined from practical considerations but at least five are preferred\"; (ii) the Standard Test Methods of the ASTM [9; 19] indicates that the average value is presented from \"the number of specimens necessary to obtain a specific level of statistical results may be determined using Test Method E122\". However, it may not be economically possible to achieve a specific confidence level and professional judgment may be necessary\". In this sense, the calculation commonly performed is the average of the UCS values obtained with each test specimen, while the artificial neural network (ANN) is a much more effective tool in reducing the error of the calculations, especially when the dispersion of the results is high. The relatively high dispersion of strength results occurs in high-porosity rocks (15-30% according to the International Association of Engineering Geology [20]), where the mechanical behavior is controlled by \(n\)[21; 22]. ANNs have been applied in rock mechanics and rock engineering to predict the behavior of a rock mass [23], the strength and deformability of intact rock from index properties such as \(v_{p}\), \(n\), corrected point load index (I\({}_{\text{s}(50)}\)) and [PERSON]'s hammer rebound (R\({}_{\text{N}}\)) [24; 25; 26; 27; 28]. [PERSON] and [PERSON] [24] developed predictive models for UCS and elastic modulus (E), adjusting neural network models based on the independent variables Schmidt hammer rebound number, point load index, water content and P-wave speed. Subsequently, the work carried out by [PERSON] et al. [25] follows a procedure similar to that of [PERSON] and [PERSON] [24], however, it is possible that the neural networks modeled in this work are over-adjusted, since the number of parameters (interactions plus threshold values) of the neural network is greater than the number of samples assigned to the network training process. Then, [PERSON] et al.