Anastasia Monia

Rock mechanics and the estimation of their material properties through field tests are important aspects and challengees in civil and geotechnical engineering. However, this procedure is expensive and difficult to attain, while the machine learning and neural network theory provide a computational tool for estimating the material properties with limited data. In this work, an estimation of the Young Modulus and the cohesion of a clayey-originated rock through feed-forward neural networks constructed from in situ data measurements is given. The input values come from the Geological Strength Index (GSI) proposed values of the point load index $I_{s50}$, the uniaxial compression strength ${\sigma }_s$, as well as the specific gravity $\gamma$ of the rock mass. The convergence analysis revealed that the convergence occurs at approximately 2000 epochs, with the largest $L_2$ mean square error norm being no greater than ${10}^{-5}$. In addition, it is demonstrated that augmenting $\gamma$ results in the estimation of rock that is stiffer and stronger. The aforementioned increase in the specific site may be up to 20% for the stiffness and up to 25% for the cohesion. This model, aside from readability and accuracy, offers the convenience of enriching it with more in situ data, thereby enhancing the flexibility of the proposed numerical tool proposed. However, its applicability is limited to the specific data acquired from the particular site, so a more general estimation requires a substantially larger dataset. Finally, the justification of the proposed model has been carried out based on suggestions from the literature for common values of clayey-oriented rock, which is fairly disintegrated as seen in the field.