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

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

doi:10.3390/ma14040794

Open AccessArticle

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

by

Ayaz Ahmad

¹

,

Furqan Farooq

^1,2,*

,

Pawel Niewiadomski

²

,

Krzysztof Ostrowski

³

,

Arslan Akbar

^4,*

,

Fahid Aslam

⁵

and

Rayed Alyousef

⁵

¹

Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Islamabad 22060, Pakistan

²

Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

³

Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland

⁴

Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong

⁵

Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

^*

Authors to whom correspondence should be addressed.

Materials 2021, 14(4), 794; https://doi.org/10.3390/ma14040794

Submission received: 28 November 2020 / Revised: 27 January 2021 / Accepted: 1 February 2021 / Published: 8 February 2021

(This article belongs to the Special Issue Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models)

Download

Browse Figures

Versions Notes

Abstract

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.

Keywords: concrete compressive strength; fly ash waste; ensemble modeling; decision tree; DT-bagging regression; cross-validation python

Graphical Abstract

Share and Cite

MDPI and ACS Style

Ahmad, A.; Farooq, F.; Niewiadomski, P.; Ostrowski, K.; Akbar, A.; Aslam, F.; Alyousef, R. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm. Materials 2021, 14, 794. https://doi.org/10.3390/ma14040794

AMA Style

Ahmad A, Farooq F, Niewiadomski P, Ostrowski K, Akbar A, Aslam F, Alyousef R. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm. Materials. 2021; 14(4):794. https://doi.org/10.3390/ma14040794

Chicago/Turabian Style

Ahmad, Ayaz, Furqan Farooq, Pawel Niewiadomski, Krzysztof Ostrowski, Arslan Akbar, Fahid Aslam, and Rayed Alyousef. 2021. "Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm" Materials 14, no. 4: 794. https://doi.org/10.3390/ma14040794

APA Style

Ahmad, A., Farooq, F., Niewiadomski, P., Ostrowski, K., Akbar, A., Aslam, F., & Alyousef, R. (2021). Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm. Materials, 14(4), 794. https://doi.org/10.3390/ma14040794

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI