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

Abstract : 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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Submitted on : Thursday, June 30, 2022 - 4:07:53 PM
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Mohamed Abdelhedi, Rateb Jabbar, Thameur Mnif, Chedly Abbes. Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions. Acta Geodynamica et Geomaterialia, Institute of Rock Structures and Mechanics, Czech Academy of Sciences, 2020, 17 (3), pp.367 - 377. ⟨10.13168/agg.2020.0027⟩. ⟨hal-03710382⟩

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