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All Journal Bulletin of Electrical Engineering and Informatics
Tayo Uthman Badrudeen
Saveetha Institute of Medical and Technical Sciences
Electrical & Electronics Engineering

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Estimation of concrete compression using regression models Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Tayo Uthman Badrudeen
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4210

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).
Co-Authors Ayodeji Olalekan Salau Tsehay Admassu Assegie