scholarly journals Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligent

2021 ◽  
pp. 100034
Author(s):  
H. Nasiri ◽  
A. Homafar ◽  
S. Chehreh Chelgani
Keyword(s):  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Modulus Of Elasticity ◽  
Artificial Intelligent

Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest

2018 ◽  
Vol 70 ◽  
pp. 980-987 ◽  
Author(s):  
S.S. Matin ◽  
L. Farahzadi ◽  
S. Makaremi ◽  
S. Chehreh Chelgani ◽  
Gh. Sattari
Keyword(s):  
Compressive Strength ◽  
Random Forest ◽  
Variable Selection ◽  
Uniaxial Compressive Strength ◽  
Modulus Of Elasticity

scholarly journals Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

2018 ◽  
Author(s):  
Naser Mahdiabadi ◽  
Gholamreza Khanlari
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Modulus Of Elasticity ◽  
Fuzzy Inference ◽  
Rock Slope Stability ◽  
Percentage Error ◽  
Inference System ◽  
Meaningful Relationship ◽  
Neuro Fuzzy

The uniaxial compressive strength (UCS) and modulus of elasticity (E) are two important rock geomechanical parameters that are widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. In the present study, prediction of UCS and E was investigated in calcareous mudstones of Aghajari Formation using multiple linear regression (MLR), multiple nonlinear regression (MNLR), artificial neural networks (ANN), and adaptive neuro-fuzzy ınference system (ANFIS). For this purpose, 80 samples from calcareous mudstones were subjected to the point loading, block punch, and cylinder punch tests. The performance of developed models was assessed based on determination coefficients (R2), mean absolute percentage error (MAPE), and variance accounted for (VAF) indices. The comparison of the obtained results revealed that, among the studied methods, ANFIS is the most suitable one for predicting UCS and E. Moreover, the results showed that ANN and MLNR respectively predict UCS and E better than MLR and a meaningful relationship between the observed and estimated UCS values in all regressions.

The Schmidt Hammer in geomorphological research

2006 ◽  
Vol 30 (6) ◽  
pp. 703-718 ◽  
Author(s):  
Andrew S. Goudie
Keyword(s):  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Modulus Of Elasticity ◽  
Rock Strength ◽  
Schmidt Hammer ◽  
Advantages And Disadvantages ◽  
Rock Hardness ◽  
Relative Dating ◽  
Young’S Modulus Of Elasticity ◽  
The 1960S

The Schmidt Hammer, originally designed for testing the hardness of concrete in 1948, was first used in a geomorphological context in the 1960s. Since then the advantages and disadvantages of the device for measuring rock characteristics have become apparent, and the Schmidt Hammer has been used for an increasing range of purposes, including the study of various weathering phenomena, the relationships between rock strength and landform, and for relative dating of a range of Holocene features. Readings of rock hardness have often been found to correlate well with other measures of rock character, such as uniaxial compressive strength and Young’s Modulus of Elasticity.

Sign in / Sign up

Export Citation Format

Share Document