The present study aims to employ modern intelligent method to predict intact rock strength parameters. This method can be used for intact rock strength parameters prediction of different extraction projects. Mechanical rock excavation projects need uniaxial compressive strength (UCS) and static modulus of elasticity (E) of the intact rock material. Many parameters affect to strength parameters, but some of them are applicable to empirical or analytical equations and the other difficulty, high-quality core samples of appropriate geometry are needed to find out these parameters. In this regard, models predicting UCS and E based on rock index tests and intact rock properties could be useful methods. This paper aims to employ Gene Expression Programming (GEP) to predict E and UCS. Out of the 44 sets of the data, 22 sets (50% of the data) were considered for training and the remaining 22 sets of the data (50%) were considered for testing. The intelligent method has been studied on the basis of data obtained from 44 different excavation projects all over the world. These parameters were collected from previous research data. The values of UCS and E are predicted by using quartz content (Q), dry density (γd) and porosity (n) of the rocks. 22 datasets (50% of the data) were utilized for modeling and the remaining 22 sets of the data (50%) were considered for evaluating theirs performance. For this purpose, writing a code was necessary, as some of the proposed relations were complex. The obtained results of this study are presented within a computer-based format in order to be easily accessible too every experts. With respect to the accuracy of the GEP method, it may be recommended for predicting intact rock strength parameters for future excavation design purpose.
In many cases the geomechanical properties of rock are required to make decisions in rock engineering projects. These properties could be unit weight, uniaxial compressive strength, tensile strength, modulus of elasticity, etc. High-quality core samples are required for laboratory tests if reliable results are desired. Such cores are not available always or it could be a time and money consuming procedure to prepare them. To overcome this difficulty encountered during the core sample preparation, some intelligent predictive models could be introduced to use the simple index parameters such as point load, block punch, Schmidt hammer and other easy accessing properties of rock to evaluate the desired properties. Among them, the models introduced by different researchers [1, 3, 5, 7, 13, 16] could be named.