2022
DOI: 10.11591/eei.v11i5.4210
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Estimation of concrete compression using regression models

Abstract: The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength.… Show more

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Cited by 3 publications
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“…(3) LR is a type of compressed estimation. By adding L1 norm penalty as a penalty function to the loss function, a more refined model is obtained that can compress some coefficients to zero [100]. The loss function formula is arg min…”
Section: Regression Modelmentioning
confidence: 99%
“…(3) LR is a type of compressed estimation. By adding L1 norm penalty as a penalty function to the loss function, a more refined model is obtained that can compress some coefficients to zero [100]. The loss function formula is arg min…”
Section: Regression Modelmentioning
confidence: 99%