Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

• Published in: Materials Vol. 14; no. 4; p. 794
• Main Authors: Ahmad, Ayaz, Farooq, Furqan, Niewiadomski, Pawel, Ostrowski, Krzysztof, Akbar, Arslan, Aslam, Fahid, Alyousef, Rayed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2021; MDPI
• Subjects: Algorithms; Bagging; Carbon dioxide; Cement industry; Civil engineering; Compressive strength; Concrete; concrete compressive strength; Construction; cross-validation python; decision tree; Decision trees; DT-bagging regression; ensemble modeling; Environmental impact; Fly ash; fly ash waste; Gene expression; Greenhouse gases; Machine learning; Mechanical properties; Model accuracy; Optimization; Reinforced concrete; Root-mean-square errors; Sugarcane; Superplasticizers; Waste materials; concrete compressive strength; decision tree; DT-bagging regression; fly ash waste; ensemble modeling; cross-validation python
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14040794

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model's accuracy and is done by R , MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.