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Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers
被引:55
作者:
Zou, Yong
[1
]
Zheng, Chao
[2
]
Alzahrani, Abdullah Mossa
[3
]
Ahmad, Waqas
[4
]
Ahmad, Ayaz
[4
,5
,6
]
Mohamed, Abdeliazim Mustafa
[7
,8
]
Khallaf, Rana
[9
]
Elattar, Samia
[10
]
机构:
[1] Wuhan Univ, Sch Civil Engn, Wuhan 430072, Peoples R China
[2] Univ Texas San Antonio, Dept Civil & Environm Engn, San Antonio, TX 78249 USA
[3] Taif Univ, Coll Engn, Dept Civil Engn, POB 11099, Taif 21944, Saudi Arabia
[4] COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad 22060, Pakistan
[5] Natl Univ Ireland Galway, Coll Sci & Engn, MaREI Ctr, Ryan Inst, Galway H91 HX31, Ireland
[6] Natl Univ Ireland Galway, Coll Sci & Engn, Sch Engn, Galway H91 HX31, Ireland
[7] Prince Sattam Bin Abdulaziz Univ, Coll Engn Al Kharj, Dept Civil Engn, Al Kharj 11942, Saudi Arabia
[8] Bayan Coll Sci & Technol, Bldg & Construct Technol Dept, Khartoum 210, Sudan
[9] Future Univ Egypt, Fac Engn & Technol, Struct Engn & Construct Management Dept, New Cairo 11845, Egypt
[10] Princess Nourah Bint Abdulrahman Univ, Coll Engn, Dept Ind & Syst Engn, POB 84428, Riyadh 11671, Saudi Arabia
来源:
关键词:
geopolymers;
concrete;
modeling;
predictions;
compressive strength;
FLY-ASH;
MECHANICAL-PROPERTIES;
SILICA-FUME;
CONCRETE;
FORMULATIONS;
BEHAVIOR;
SODIUM;
D O I:
10.3390/gels8050271
中图分类号:
O63 [高分子化学(高聚物)];
学科分类号:
070305 ;
080501 ;
081704 ;
摘要:
The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R-2), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R-2 value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models' lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials.
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页数:23
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