Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Madiha Anjum; Kaffayatullah Khan; Waqas Ahmad; Ayaz Ahmad; Muhammad Nasir Amin; Afnan Nafees

doi:10.3390/polym14183906

Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Polymers (Basel). 2022 Sep 19;14(18):3906. doi: 10.3390/polym14183906.

Authors

Madiha Anjum¹, Kaffayatullah Khan², Waqas Ahmad³, Ayaz Ahmad⁴, Muhammad Nasir Amin², Afnan Nafees³

Affiliations

¹ Department of Computer Engineering, College of Computer Science and Information, Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
² Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
³ Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
⁴ MaREI Centre, Ryan Institute and School of Engineering, College of Science and Engineering, National University of Ireland Galway, H91 TK33 Galway, Ireland.

Abstract

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study's aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R²), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R² for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC's CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients.

Keywords: compressive strength; concrete; fiber-reinforced concrete; nano-silica; nano-silica modified concrete.

Grants and funding

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. 1323], through its KFU Research Summer Initiative./King Faisal University