Effects of lithofacies and environment on
            <i>in situ</i>
            and laboratory Schmidt hammer tests: a case study of carbonate rocks

Arman, Hasan; Abdelghany, Osman; Hashem, Waheed; Aldahan, Ala

doi:10.1144/qjegh2016-049

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…Many researchers have been questioning its destructive nature and the potential complications involved in handling, preparing, and testing the samples, particularly in weak rocks. Nevertheless, even for hard rocks like carbonates (limestone), the UCS test is still very difficult, expensive, tedious, and time-consuming, and it requires to test a number of well-prepared rock samples in order to obtain consistent strength value for the rock material [3][4][5][6][7][8] . Frequently, nondestructive indirect methods like Schmidt hammer, Shore hardness, slake durability, P-wave velocity, point load index test, etc.…”

Section: Several Machine Learning Techniques Comparison For the Predi...mentioning

confidence: 99%

“…These methods have been continuously used as an alternative method since they are simple, more practical and do not require complicated procedures on the testing equipment. Besides, these methods are faster and more economical compared to the traditionally-suggested UCS testing methods 6,[9][10][11][12][13][14][15][16][17][18][19] .…”

Section: Several Machine Learning Techniques Comparison For the Predi...mentioning

confidence: 99%

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Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks

Hassan

Arman

2022

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In engineering practices, it is critical and necessary to either measure or estimate the uniaxial compressive strength (UCS) of the rock. Measuring the UCS of rocks requires comprehensive studies in the field and in the laboratory for the rock block sampling, coring, and testing. These studies are time-consuming, expensive and go through difficult processes. Alternatively, the UCS can either be estimated by empirical relationships or predictive models with various measured mechanical and physical parameters of the rocks. Previous studies used different methods to predict UCS, including least squares regression techniques (MLR), adaptive neuro-fuzzy inference system (ANFIS), Sequential artificial neuron networks (SANN), etc. This study is intended to estimate the UCS of the carbonate rock by using a simple, measured Schmidt Hammer (SHVC) test on core sample and a unit weight (γn) of carbonate rock. Principal components regression (PCR), MLR, SANN, and ANFIS are employed to predict the UCS. We are not aware of any study compared the performances of these methods for the prediction of the UCS values. Based on the root mean square error, mean absolute error and R2, the Sequential artificial neural network has a slight advantage against the other three models.

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Section: Several Machine Learning Techniques Comparison For the Predi...mentioning

confidence: 99%

Section: Several Machine Learning Techniques Comparison For the Predi...mentioning

confidence: 99%

Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks

Hassan

Arman

2022

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“…Nummulitic limestone with marl beds is also available in different localized outcrops. The Early Oligocene Asmari Formation, aged 34–29 Myr, comprising mainly gypsiferous mudstone, nummulitic marly limestone, chalky and dolomitic limestone, and marl, is the youngest rock unit in the study area 22 .…”

Section: Study Area and Geological Settingsmentioning

confidence: 99%

Correlation of P-wave velocity with mechanical and physical properties of limestone with statistical analysis

Arman

2021

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This study aims to investigate the correlation between the P-wave velocity (Vp) and the mechanical and the physical properties of the limestone; Vp tests were conducted on over 320 limestone samples. Moreover, the effects of the mineralogical, textural, and chemical composition of limestone were also studied through thin sections, scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray fluorescence (XRF). The relationships between the Vp and the uniaxial compressive strength (UCS), point load index (PLI(Is(50)), 2nd cycle of slake durability index (Id2), natural unit weight (γn), specific gravity (Gs(c)), water absorption by weight (WA), and porosity (n) were estimated using representative empirical equations. The empirical equations were validated by Student’s t test that has indicated the existence of strong relationships between the mechanical and physical properties of the intact limestone with Vp; the calculated t-values were higher than the t-critical value. Furthermore, the results of previously available studies were compared with the results of this study in terms of the generated equations for Vp values and the slope of a 1:1 line, which was used to appraise the predicted and measured values. This study demonstrates that the UCS, PLI(Is(50)), Id2, γn, Gs(c), WA, and n values of an intact limestone can be predicted by using Vp, which is fast, easy, economical and nondestructive test.

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“…Inhomogeneity and rapid changes in the physical properties of rock masses commonly cause unexpected rock failure, particularly when these factors are combined with the presence of water. All these factors underline the importance of measuring the rock strength via in situ or laboratory methods (Marinos and Hoek 2001;Erguler and Ulusay 2009;Yilmaz 2010;Arman et al 2013aArman et al , b, 2014Arman et al , 2017.…”

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Strength estimation of evaporitic rocks using different testing methods

et al. 2019

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Rock strength is defined as the limit of the ability of a rock to resist stress or deformation without breaking. Testing methods recommended by ISRM (International Society of Rock Mechanics) and ASTM (American Standards Testing Material) include unconfined compressive strength (UCS), point load index (PLI), indirect tensile strength (ITS), Schmidt hammer rebound (SHR), sonic velocity (V p and V s ), and slake durability index 2nd cycle (I d2 ). This contribution compares the results of these methods and explores the influence of rock composition and texture on Lower Miocene evaporites from Al Ain city, United Arab Emirates (UAE). These sedimentary rocks are common in the Arabian Peninsula as exposures or in the subsurface where they may constitute the foundations of buildings. A large number of UCS, PLI, ITS, SHR, SV, and I d2 tests were carried out on both core samples and rock blocks according to ASTM Standards. Examination of compositional and textural characteristics of representative rock samples was performed using XRD, XRF, polarized-light microscopy, and SEM. The results reveal variable correlations between the rock strength parameters with specific significant values between 0.53 and 0.72. The effect of composition and texture of the evaporitic rocks on their strength behavior is related to impurities such as clay minerals and celestite and grain interlocking textures. Despite the limited compositional variability of the evaporitic rocks (5-10%), the textural variability may present a challenging feature in rock strength testing and should be taken as a primary factor for consideration during applications.

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Effects of lithofacies and environment on in situ and laboratory Schmidt hammer tests: a case study of carbonate rocks

Cited by 14 publications

References 27 publications

Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks

Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks

Correlation of P-wave velocity with mechanical and physical properties of limestone with statistical analysis

Strength estimation of evaporitic rocks using different testing methods

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