Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

Author:

Ashraf Muhammad Waqas1,Khan Adnan2,Tu Yongming13,Wang Chao3,Ben Kahla Nabil4,Javed Muhammad Faisal56,Ullah Safi17,Tariq Jawad8

Affiliation:

1. Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, National Engineering Research Center for Prestressing Technology, School of Civil Engineering, Southeast University , 211189 , Nanjing , China

2. School of Transportation, Southeast University, Jiulonghu Campus, Jiangning District , Nanjing , Jiangsu, 211189 , China

3. Division of Structural and Fire Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology , SE-97187 , Luleå , Sweden

4. Department of Civil Engineering, College of Engineering, King Khalid University, PO Box 394, Abha, 61411, Saudi Arabia; Center for Engineering and Technology Innovations, King Khalid University , Abha , 61421 , Saudi Arabia

5. Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus , Islamabad , Pakistan

6. Department of Technical Science, Western Caspian University , Baku , Azerbaijan

7. Department of Construction Management, Global Banking School (Bath Spa University), Devonshire Street North , Manchester , M12 6JH , United Kingdom

8. School of Civil Engineering, Henan University of Technology , Zhengzhou , Henan, 450001 , China

Abstract

Abstract Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

Publisher

Walter de Gruyter GmbH

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