Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity

2008 ◽  
Vol 67 (4) ◽  
pp. 491-498 ◽  
Author(s):  
İbrahim Çobanoğlu ◽  
Sefer Beran Çelik
Keyword(s):  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Wave Velocity ◽  
Point Load ◽  
P Wave ◽  
Point Load Strength ◽  
P Wave Velocity ◽  
Schmidt Hardness

scholarly journals Correlation of uniaxial compressive strength with the dynamic elastic modulus, P - wave velocity and S - wave velocity of different rock types

2019 ◽  
pp. 11-25
Author(s):  
Jelena Majstorović ◽  
Miloš Gligorić ◽  
Suzana Lutovac ◽  
Milanka Negovanović ◽  
Luka Crnogorac
Keyword(s):  
Compressive Strength ◽  
Elastic Modulus ◽  
Uniaxial Compressive Strength ◽  
Wave Velocity ◽  
P Wave ◽  
Dynamic Elastic Modulus ◽  
S Wave ◽  
Rock Types ◽  
P Wave Velocity

scholarly journals Estimating uniaxial compressive strength, density and porosity of rocks from the p-wave velocity measurements in-situ and in the laboratory

2021 ◽  
Vol 74 (4) ◽  
pp. 521-528
Author(s):  
André Cezar Zingano ◽  
Paulo Salvadoretti ◽  
Rafael Ubirajara Rocha ◽  
João Felipe Coimbra Leite Costa
Keyword(s):  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Wave Velocity ◽  
P Wave ◽  
Velocity Measurements ◽  
P Wave Velocity

scholarly journals Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine

2020 ◽  
Vol 10 (13) ◽  
pp. 4565
Author(s):  
Manuel Saldaña ◽  
Javier González ◽  
Ignacio Pérez-Rey ◽  
Matías Jeldres ◽  
Norman Toro
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Statistical Analysis ◽  
Uniaxial Compressive Strength ◽  
Wave Velocity ◽  
Accurate Determination ◽  
Effective Porosity ◽  
P Wave ◽  
Strength Parameter ◽  
P Wave Velocity

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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