Journal Article

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

Niewiadomski, Pawel, Alyousef, Rayed, Akbar, Arslan, Farooq, Furqan, Aslam, Fahid, Ostrowski, Krzysztof, Ahmad, Ayaz

2021

Overview

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 R2 = 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 R2, 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.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Bagging / Carbon dioxide / Cement industry / Civil engineering / Compressive strength / Concrete / Construction / Decision trees / Environmental impact / Fly ash / Gene expression / Greenhouse gases / Machine learning / Mechanical properties / Model accuracy / Optimization / Reinforced concrete / Root-mean-square errors / Sugarcane / Superplasticizers / Waste materials