TITLE:
Statistical Analyses and Geotechnical Evaluation of Nubia Sandstone, Golden Triangle Area, Egypt
AUTHORS:
Hesham Ahmed Hussein Ismaiel, Mohamed Mohamed Askalany, Ali Ismail Ali
KEYWORDS:
Geotechnical Evaluation, Empirical Equations, Statistical Analyses, Nubia Sandstone, Non-Destructive Test
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.9 No.11,
November
16,
2021
ABSTRACT: Great efforts had been made to use indirect non-destructive tests in the
geotechnical evaluation of rocks, especially sandstones, employing different
empirical equations. However, most of these equations have been derived from
hard and compacted sandstones data; therefore, the focus of this research is on weak and
weakly compacted sandstones, aiming firstly to obtain empirical equations for
estimating their characteristics, secondly to demonstrate and visualize the
correlations between the studied variables, and finally to cluster the studied samples based on their
characteristics. To attain these aims, twenty oriented block samples were collected from Nubia
sandstone, central Eastern Desert, Golden Triangle area, Egypt. These samples
were prepared and tested according to standard test methods, including uniaxial
compressive strength (UCS), Brazilian tensile strength (BTS), Schmidt rebound
number (SRN), porosity (n), bulk density (ρ), and ultrasonic P-wave velocity
(UPV). The loss on ignition (LOI) was also employed as a physicochemical test
for classifying the studied samples and indicating pores filling materials. The
results revealed that these sandstones are characterized mainly by high n,
low ρ, and low UPV values and
these give an indication of weakly compacted and weakly cemented sandstone with
shallow burial diagenetic conditions. Based on UCS and elastic modulus values,
these sandstones are mainly classified as very low strength and highly yielding
rocks. The results of regression analysis show satisfactory correlations
between physical and mechanical characteristics, indicating the suitability of
obtained empirical equations to deduce these properties. Principal component
analysis revealed that the LOI, BTS, SRN, and USC have a positive correlation
to each other and weakly correlated with ρ and UPV,
which positively correlated to each other and negatively correlated to n.
The results of agglomerative hierarchical clustering revealed that the studied
samples can group into three main clusters depending on their USC, LOI, and n values.