Estimation of concrete compression using regression models

Tsehay Admassu Assegie, Ayodeji Olalekan Salau, Tayo Uthman Badrudeen

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. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).

Keywords


Concrete compression; Concrete strength; Concrete strength prediction; Concrete structure; Machine learning

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DOI: https://doi.org/10.11591/eei.v11i5.4210

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