Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

Hisham Jahangir Qureshi; Muhammad Umair Saleem; Muhammad Faisal Javed; Abdulrahman Fahad Al Fuhaid; Jawad Ahmad; Muhammad Nasir Amin; Kaffayatullah Khan; Fahid Aslam; Md Arifuzzaman

doi:10.3390/ma15217412

Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches

Materials (Basel). 2022 Oct 22;15(21):7412. doi: 10.3390/ma15217412.

Authors

Hisham Jahangir Qureshi¹, Muhammad Umair Saleem², Muhammad Faisal Javed³, Abdulrahman Fahad Al Fuhaid¹, Jawad Ahmad⁴, Muhammad Nasir Amin¹, Kaffayatullah Khan¹, Fahid Aslam⁵, Md Arifuzzaman¹

Affiliations

¹ Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
² Service Stream Limited Co., Ltd., Chatswood, NSW 2067, Australia.
³ Department of Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan.
⁴ Department of Civil Engineering, Swedish College of Engineering, Wah Cantt 47070, Pakistan.
⁵ Department of Civil Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Abstract

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the n^th estimators to achieve a robust R². Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R² of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

Keywords: autogenous shrinkage; concrete; ensemble models; machine learning approaches; materials; statistical measures; super absorbent Polymer; validation analysis.

Grants and funding

Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Project No: GRANT 801).