scholarly journals Evaluation of geotechnical properties, petrographic characteristics and ultrasonic wave velocity of weak sandstones

Author(s):  
Waleed Abdelmoghny Metwaly Ogila ◽  
Mohamed H. Abdel Aal ◽  
Sahar M. Abd El Bakey

Abstract Several new settlements have been constructed in the desert areas of Egypt. These new settlements are composed of many new types of problematic formations that creating many geological and engineering challenges. One of the most problematic formations is weak sandstones that are characterized by low mechanical strength and bearing capacity as well as high deformability and fracturing. The physico-mechanical characteristics of these sandstones are the most crucial parameters in design and stability evaluation of any surface and underground engineering structures. The determination of these parameters is complicated, difficult and time consuming as well as is required a great accuracy in sample preparation and testing procedure, and is considered as expensive testing. The objective of this study is analyzing the shallow marine weak sandstones to determine the best and significant correlations of petrographic characteristics and physical engineering index properties that may be useful for estimating unconfined compressive strength (UCS), uniaxial pore volume compressibility (mv), compressional P-wave velocity (Vp) and dynamic constrained modulus (Es). As well as predicting the UCS and mv from compressional P-wave velocity test and estimating the petrographic parameters from the physical engineering index parameters. The present study revealed that the studied samples are high to very high porous sandstones with very low to moderate density, and classified as extremely weak to weak rocks with very high deformability and very low wave velocity. In this study, the physical properties form non-significant linear relations with UCS, Vp, and Es as well as there are moderately strong to strong correlations between P-wave velocity, constrained modulus, unconfined compressive strength and compressibility characteristics. Based on the regression analysis, the dolomite cement and matrix contents, quartz and rock fragments contents, packing density and proximity, sorting, roundness, mean grain size, and grain to cement contact exhibited weak to strong statistical correlations with mechanical properties of the studied sandstones. These relationships revealed that due to the mineralogical, textural, and microstructure variations of the studied recycle origin sandstones, the non-significant to significant relations are resulted. In this study, the backward multiple regression was applied to predict the UCS, mv, Vp and Es of the studied sandstones by selecting some physical and petrographic characteristics which exhibit statistically significant correlations with them. The results of this study were presented in the form of predictive models and equations.

2021 ◽  
Vol 74 (4) ◽  
pp. 521-528
Author(s):  
André Cezar Zingano ◽  
Paulo Salvadoretti ◽  
Rafael Ubirajara Rocha ◽  
João Felipe Coimbra Leite Costa

2020 ◽  
Vol 10 (13) ◽  
pp. 4565
Author(s):  
Manuel Saldaña ◽  
Javier González ◽  
Ignacio Pérez-Rey ◽  
Matías Jeldres ◽  
Norman Toro

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 travertine, 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.


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