Effect of loading frequency on the dynamic properties of sand-tire‌ mixture.

Uniaxial compressive strength (UCS) represents one of the key mechanical properties used to characterize rocks along with the other important properties of porosity and density. While several studies have proved the accuracy of artificial intelligence in modeling UCS, some authors believe that the use of artificial intelligence is not practical in predicting. The present paper highlights the ability of an artificial neural network (ANN) as an accurate and revolutionary method with regression models, as a conventional statistical analysis, to predict UCS within carbonate rocks and mortar. Thus, ANN and multiple linear regressions (MLR) were applied to estimate the UCS values of the tested samples. For experimentation we carried out ultrasonic measurements on cubic samples before testing uniaxial compressive strength perpendicularly to the stress direction. The models were performed to correlate effective porosity, density and ultrasonic velocity to the UCS measurements. The resulting models would allow the prediction of carbonate rocks and mortar's UCS values usually determined by laborious experiments. Although the results demonstrate the usefulness of the MLP method as a simple, practical and economical model, the ANN model is more accurate.

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Effect of loading frequency on the dynamic properties of sand-tire‌ mixture.

Cited by 4 publications

References 28 publications

Numerical Investigation of Liquefaction Resistance of Buried Pipelines with Sand-Tire Mixture Using Critical State Two-Surface Plasticity Model

Numerical Investigation of Liquefaction Resistance of Buried Pipelines with Sand-Tire Mixture Using Critical State Two-Surface Plasticity Model

Liquefaction Resistance of Silty Sand with Ground Rubber Additive

Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions

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