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

Title:

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

Creator:

Abdelhedi, Mohamed, Jabbar, Rateb, Mnif, Thameur, and Abbes, Chedly

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ed8e714a-b201-4e35-bff8-3630c3a87f35
uuid:ed8e714a-b201-4e35-bff8-3630c3a87f35
doi:10.13168/AGG.2020.0027

Subject:

multiple linear regressions, carbonate rocks, artificial neural network, UCS, mortar, and density

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

367-377

Source:

Acta geodynamica et geomaterialia : | 2020 Volume:17 | Number:3 (199)

Harvested from:

CDK

Metadata only:

false