ISRM Suggested method for determination of the Schmidt hammer rebound hardness: Revised version

Aydın, Adnan

doi:10.1016/j.ijrmms.2008.01.020

Cited by 203 publications

(105 citation statements)

References 5 publications

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“…Generally, rebound surface hardness was used to approximately predict the compressive strength or empirical constitutive relation of concrete [18][19][20]. In this study, however, close attention was only paid to the variation of rebound index due to sulfate attack and related mechanism without considering the relation between the surface hardness and compressive strength.…”

Section: Introductionmentioning

confidence: 99%

Evolution of surface hardness of concrete under sulfate attack

Ouyang

Chen

Jiang

2014

Construction and Building Materials

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Section: Introductionmentioning

confidence: 99%

Evolution of surface hardness of concrete under sulfate attack

Ouyang

Chen

Jiang

2014

Construction and Building Materials

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“…Release mechanism destroyed is 6.48 Mpa, indicating that a gypsum sample with 40 % moisture content is strong enough not to be shattered during the collision process (Ulusay and Hudson, 2007;Aydin, 2009). …”

Section: Right Camera Cushionmentioning

confidence: 99%

The effects of gravel cushion particle size and thickness on the coefficient of restitution in rockfall impacts

Zhu

Wang

Xia

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Gravel cushions are widely used to absorb the impact energy of falling rocks in open-pit mines. A particularly important application is to enhance the energy-absorbing capacity of rockfall sheds. In this paper, we study how varying the thickness and particle size of a gravel cushion influences its energy-consumption and buffering effects. We performed a series of laboratory drop tests by dropping blocks from a fixed height onto cushions of different thicknesses and particle sizes. The results indicate that, for a given impact energy, the cushion thickness has a strong influence on the measured coefficient of restitution (COR) and therefore impact pressure. Additional tests were performed to study how the radius of the block and the height it is dropped from affect the measured COR. This showed that as the movement height of the block is increased the COR also increases, and blocks with larger radii exhibit a larger variability in measured COR. Finally, we investigated the influence of rockfall block radius, r, movement height, H , cushion thickness, h, and particle size, d, on the COR and the damage depth, L, of the cushion. The test results reveal that the cushion thickness is the primary design parameter, controlling not only COR, but also the stability of the cushion material. The results provide a theoretical and practical basis for the design of gravel cushions for rockfall protection.

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“…The rebound value for each surface is calculated from the average. The JCS of the specimen is then calculated according to Aydin (2009). The basic-friction angle (φ b ) is measured using tilt tests on saw-cut dry joint surfaces.…”

Section: Mechanical Propertiesmentioning

confidence: 99%

Estimating the three-dimensional joint roughness coefficient value of rock fractures

2017

Bull Eng Geol Environ

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Measurement and estimation of the joint roughness coefficient (JRC) is a critical but also difficult challenge in the field of rock mechanics. Parameters for estimating JRC based on a profile derived from a fracture surface are generally twodimensional (2D), where a single or multiple straight profiles derived from a surface cannot reflect the roughness of the entire surface. It is therefore necessary to derive the threedimensional (3D) roughness parameters from the entire surface. In this article, a detailed review is made on 3D roughness parameters along with classification and discussion of their usability and limitations. Methods using Triangulated Irregular Network (TIN) and 3D wireframe to derive 3D roughness parameters are described. Thirty-eight sets of fresh rock blocks with fractures in the middle were prepared and tested in direct shear. Based on these, empirical equations for JRC estimation using 3D roughness parameters have been derived. Nine parameters (θ s , θ g , θ 2s , S sT , S sF , V an , Z sa , Z rms , and Z range ) are found to have close correlations with JRC and are capable of estimating JRC of rock fracture surfaces. Other parameters (Z ss , Z sk , V svi , V sci , S dr and S ts ) show no good correlations with JRC. The sampling interval has little influence when using volume and amplitude parameters (V an , Z sa , Z rms , and Z range ) for JRC estimation, while it influences to some extent when other parameters (θ s , θ g , θ 2s , S sT and S sF ) are used. For their easy calculation, the equations with amplitude parameters are recommended to facilitate rapid estimation of JRC in engineering practice.

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ISRM Suggested method for determination of the Schmidt hammer rebound hardness: Revised version

Cited by 203 publications

References 5 publications

Evolution of surface hardness of concrete under sulfate attack

Evolution of surface hardness of concrete under sulfate attack

The effects of gravel cushion particle size and thickness on the coefficient of restitution in rockfall impacts

Estimating the three-dimensional joint roughness coefficient value of rock fractures

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