Evolutionary Algorithms for Strength Prediction of Geopolymer Concrete

被引:2
|
作者
Huang, Bingzhang [1 ,2 ]
Bahrami, Alireza [3 ]
Javed, Muhammad Faisal [4 ]
Azim, Iftikhar [5 ]
Iqbal, Muhammad Ayyan [6 ]
机构
[1] Liuzhou Inst Technol, Sch Civil Engn & Architecture, Liuzhou 545004, Peoples R China
[2] Guangxi Prefabricated Bldg Life Cycle Management &, Liuzhou 545004, Peoples R China
[3] Univ Gavle, Fac Engn & Sustainable Dev, Dept Bldg Engn Energy Syst & Sustainabil Sci, S-80176 Gavle, Sweden
[4] GIK Inst Engn Sci & Technol, Dept Civil Engn, Topi 23460, Swabi, Pakistan
[5] Govt Khyber Pakhtunkhwa, Publ Hlth Engn Dept, Peshawar 25000, Pakistan
[6] Univ Engn & Technol, Dept Civil Engn, Lahore 39161, Pakistan
关键词
geopolymer concrete; compressive strength; split tensile strength; prediction model; evolutionary algorithm; ARCH ACTION CAPACITY; MODEL;
D O I
10.3390/buildings14051347
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Geopolymer concrete (GPC) serves as a sustainable substitute for conventional concrete by employing alternative cementitious materials such as fly ash (FA) instead of ordinary Portland cement (OPC), contributing to environmental and durability benefits. To increase the rate of utilization of FA in the construction industry, distinctive characteristics of two machine learning (ML) methods, namely, gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to propose precise prediction models for the compressive strength and split tensile strength of GPC comprising FA as a binder. A comprehensive database was collated, which comprised 301 compressive strength and 96 split tensile strength results. Seven distinct input variables were employed for the modeling purpose, i.e., FA, sodium hydroxide, sodium silicate, water, superplasticizer, and fine and coarse aggregates contents. The performance of the developed models was assessed via numerous statistical metrics and absolute error plots. In addition, a parametric analysis of the finalized models was performed to validate the prediction ability and accuracy of the finalized models. The GEP-based prediction models exhibited better performance, accuracy, and generalization capability compared with the MEP-based models in this study. The GEP-based models demonstrated higher correlation coefficients (R) for predicting the compressive and split tensile strengths, with the values of 0.89 and 0.87, respectively, compared with the MEP-based models, which yielded the R values of 0.76 and 0.73, respectively. The mean absolute errors for the GEP- and MEP-based models for predicting the compressive strength were 5.09 MPa and 6.78 MPa, respectively, while those for the split tensile strengths were 0.42 MPa and 0.51 MPa, respectively. The finalized models offered simple mathematical formulations using the GEP and Python code-based formulations from MEP for predicting the compressive and tensile strengths of GPC. The developed models indicated practical application potential in optimizing geopolymer mix designs. This research work contributes to the ongoing efforts in advancing ML applications in the construction industry, highlighting the importance of sustainable materials for the future.
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页数:26
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