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.

Designing petroleum tanks that are inter-related with pipelines in a founding position containing very compressible soil are a challenging task, particularly when a possible high-water table and considerably high seismicity are also present. Some of the issues that occur are the problems of the time dependence of settlement behavior as well as the earthquake response of the soil–structure interaction system. This work intends to portray the following: (a) an estimation of the foundations and the corresponding consolidation time response and (b) the earthquake-induced geohazard as well as the soil–structure interaction considered for the foundation of the tanks alongside pipelines in a suburban area in Greece, namely Kalochori of Thessaloniki. A numerical analysis considering the dynamic and static behavior and inter-relation among the soil mass and the buried pipeline alongside the system of the foundation type and the soil beneath is performed. Also, the foundation type that was finally chosen as the optimized solution, namely the set of gravel piles and the subsequent prestress loading, is presented and discussed. Taking into account that the soil in the vicinity of the foundation will have its shear strength during an earthquake decreased to almost zero, risk reduction actions may be suggested for large stresses imposed on the pipeline. The methodology for the dimensionality of the gravel pile group as well as the preloading session indicate that a substantial reduction in the displacements of more than 50% is obtained with the combination of the methods, something that would not be feasible if the methods were implemented individually.