Matches in SemOpenAlex for { <https://semopenalex.org/work/W4206133269> ?p ?o ?g. }
- W4206133269 endingPage "845" @default.
- W4206133269 startingPage "845" @default.
- W4206133269 abstract "Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time." @default.
- W4206133269 created "2022-01-26" @default.
- W4206133269 creator A5000188421 @default.
- W4206133269 creator A5041815520 @default.
- W4206133269 creator A5048942846 @default.
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- W4206133269 creator A5065711006 @default.
- W4206133269 creator A5087374064 @default.
- W4206133269 creator A5091151166 @default.
- W4206133269 date "2022-01-12" @default.
- W4206133269 modified "2023-09-26" @default.
- W4206133269 title "Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach" @default.
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