Estimation of rock engineering properties using hardness tests

Shalabi, Faisal I.; Cording, Edward J.; Al-Hattamleh, Omar

doi:10.1016/j.enggeo.2006.12.006

Cited by 184 publications

(42 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various studies have indicated strong empirical relations between Schmidt Hammer R values and measured Young's Modulus (Katz et al, 2000), with the coeffi cient of determination (R 2 ) values as high as 0·99 (see also Aggistalis et al, 1996;Sachpazis, 1990;Yagiz, 2009). In addition to compressive strength and Young's Modulus, attempts have been made to determine the correlation between R values and other measures of rock physical properties, including the point load index (Aggistalis et al, 1996) and the Shore Scleroscope (Yaşar and Erdogan, 2004;Shalabi et al, 2007).…”

Section: The Classic Schmidt Hammermentioning

confidence: 99%

The use of the Schmidt Hammer and Equotip for rock hardness assessment in geomorphology and heritage science: a comparative analysis

Viles

Goudie

Grab

et al. 2011

Earth Surf Processes Landf

209

171

View full text Add to dashboard Cite

Rapid, fi eld-based measurements of rock hardness are of use in investigating many geomorphological and heritage science problems. Several different methods are now available for taking such measurements, but little work has been done to assess their comparability and strengths and weaknesses. We review here the capabilities of two types of Schmidt Hammer (Classic N type and Silver Schmidt BL type) alongside two types of Equotip (standard type D and Piccolo) for investigating rock hardness in relation to rock weathering on various types of sandstone and limestone, as well as basalt and dolerite. Whilst the two Schmidt hammers and the two Equotips show comparable results when tested at 15 individual sites, interesting differences are found between the Equotip and Schmidt Hammer values which may reveal information about the nature of weathering on different surfaces. Operator variance is shown to be an issue in particular for the Equotip devices, which also exhibit higher variability in measurements and necessitate larger sample sizes. Carborundum pre-treatment also has varying effects on the data collected, depending on the nature of the surface studied. The Equotip devices are shown to be particularly useful on smaller blocks and in situations where edge effects may affect Schmidt Hammer readings. We conclude that whilst each device contributes to geomorphological research, they do not necessarily produce comparable information. Indeed, using Schmidt Hammer and Equotip in combination and looking at any differences in results may provide invaluable insights into the structure of the near-surface zones and the nature of weathering processes.

show abstract

Section: The Classic Schmidt Hammermentioning

confidence: 99%

The use of the Schmidt Hammer and Equotip for rock hardness assessment in geomorphology and heritage science: a comparative analysis

Viles

Goudie

Grab

et al. 2011

Earth Surf Processes Landf

209

171

View full text Add to dashboard Cite

show abstract

“…Shalabi et al (2007) proved that the relationships from this test were weaker than the results obtained by Schmidt hammer and shore tests.…”

Section: Ucs Of Rocks: Measurement and Prediction Methodsmentioning

confidence: 82%

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

2012

View full text Add to dashboard Cite

Uniaxial Compressive Strength (UCS) is considered as one of the most important parameters in designing rock structures. Determination of this parameter requires preparation of rock samples which is costly and time consuming. Moreover discrepancy of laboratory test results is often observed. To overcome the drawbacks of traditional method of UCS measurement, in this paper, predictive models based on neuro-genetic approach and multivariable regression analysis have been developed for predicting compressive strength of different type of rocks. Coefficient of determinatoin (R2) and the Mean Square Error (MSE) were calculated for comparison of the models' efficiency. It was observed that accuracy of the neuro-genetic model is significantly better than regression model. For the neuro-genetic and regression models, R2 and MSE were equal to 95.89 % and 0.0045 and 77.4 % and 1.61, respectively. According to sensitivity analysis for neurogenetic model, Schmidt rebound number is the most effective parameter in predicting UCS.

show abstract

“…The obtained R values are often related to the mechanical strength of rock (Cargill and Shakoor 1990;Sachapazis 1990;Katz et al 2000;Yilmaz and Sendir 2002;Yasar and Erdogan 2004;Aydin and Basu 2005;Buyuksagis and Goktan 2007;Shalabi et al 2007;Ç obanoglu and Ç elik 2008;Gupta 2009;Saptono et al 2013;Tandon and Gupta 2014;Karaman and Kesimal 2014), weathering grade (Karpuz and Pasamehmetoglu 1977;Matthews and Shakesby 1984;White et al 1998;Gokceoglu and Aksoy 2000;Aydin and Basu 2005;Goudie 2006; Arıkan et al 2007;Borelli et al 2007), spacing of discontinuities (Young and Fowell 1978;Greco and Sorriso-Valvo 2005), joint wall strength (ISRM 2007), drillability (Kahraman 1999;Kahraman et al 2000Kahraman et al , 2003, rippability (Karpuz 1990), tunneling machine performance (Poole and Farmer 1978;Howarth et al 1986;Bilgin et al 1990; Goktan and Gunes 2005), abrasivity and abrasion resistance of rocks (Kazi and Al-Mansour 1980;Janach and Merminod 1982;Yavuz et al 2008), and p-wave velocity (Kahraman 2001;Sharma et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Influence of rock cradle block geometry on rebound hardness

Yılmaz

Göktan

Yavuz

et al. 2015

Bull Eng Geol Environ

View full text Add to dashboard Cite

The Schmidt hammer (SH) testing of cylindrical rock cores requires the use of a cradle block (core holder) to hold the cores firmly during the test. For this purpose, the related International Society for Rock Mechanics (ISRM)-suggested method and American Society for Testing and Materials (ASTM) standard suggest the use of an 'arch-shaped' or a 'V-shaped' cradle block. However, there is little or no detailed quantitative information in the literature regarding the potential influence of cradle block type on the measured rebound values (R values). With this in view, an investigation was carried out regarding the influence of cradle block geometry on R values, by employing the two cradle blocks specified in the preceding Standards. Using an L-type Digi-Schmidt hammer, SH tests were performed on NX-size cylindrical cores of 20 different rock varieties, both in 'oven-dry' and 'wet' state. Although the observed differences were usually small, results showed that the magnitudes of the mean rebound values determined in the arch-shaped block (R A ) were higher than those determined in the V-shaped block (R V ), except in one case. For the oven-dried samples, lowest and highest differences between the means of R A and R V values were 0.37 and 5.92, respectively. In the case of wet samples, the differences ranged from a low of 0.33 to a high of 4.70 in R-units. Statistically significant positive linear correlations were observed between R A and R V values, although the degree of correspondence was relatively lower for rocks having uniaxial compressive strength \50 MPa. The results of additional SH tests carried out on two different engineering materials also indicated that higher mean rebound values could be attained in the arch-shaped block. The results of the present study did not confirm the findings of a previous study where it was found that the V-shaped cradle block gave consistently higher mean rebound values than the arch-shaped one. The possible reasons for this discrepancy were discussed.

show abstract

Estimation of rock engineering properties using hardness tests

Cited by 184 publications

References 9 publications

The use of the Schmidt Hammer and Equotip for rock hardness assessment in geomorphology and heritage science: a comparative analysis

The use of the Schmidt Hammer and Equotip for rock hardness assessment in geomorphology and heritage science: a comparative analysis

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Influence of rock cradle block geometry on rebound hardness

Contact Info

Product

Resources

About