气候变化领域集成服务门户(CCPortal): Intelligent optimization for modeling carbon dioxide footprint in fly ash geopolymer concrete: A novel approach for minimizing CO2 emissions

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DOI	10.1016/j.jece.2023.111835
	Intelligent optimization for modeling carbon dioxide footprint in fly ash geopolymer concrete: A novel approach for minimizing CO2 emissions
	Wudil, Y. S.; Al-Fakih, Amin; Al-Osta, Mohammed A.; Gondal, M. A.
发表日期	2024
ISSN	2213-2929
EISSN	2213-3437
起始页码	12
结束页码	1
卷号	12 期号:1
英文摘要	In the global effort to mitigate climate change and reduce CO2 emissions, this study introduces an innovative, pioneering approach that combines artificial intelligence and experimental methods to investigate the CO2 footprint (CO2-FP) in fly ash geopolymer concrete materials. Three powerful non-linear intelligent learners, including Gaussian Process Regression (GPR) with Response Surface Methodology (RSM), Support Vector Regression (SVR), and Standalone Decision Tree Regression (DTR) are employed. The models are developed using seven input features related to the curing temperature, fly ash content, concentrations of coarse and fine aggregates, alkaline activators (Na2SiO3, NaOH) content, and superplasticizer. To identify the most influential input features, three different combinations (combo-1, combo-2, and combo-3) of these features are utilized in model building. The models' performance is assessed using key metrics such as coefficient of correlation (CC), Nash Sutcliffe coefficient efficiency (NSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). During the verification phase, the GPR-3 [Combo-3] model emerges as the most efficient in predicting the CO2-FP, with a high CC value of 0.9645 and NSE value of 0.9292. Consistently, Combo-3 demonstrates superior performance across all the models, underscoring the significance of the selected features. The findings of this study provide valuable guidance to industries and policymakers, enabling them to optimize concrete compositions and minimize CO2 emissions, thus contributing to global environmental sustainability.
英文关键词	Carbon dioxide (CO2); Artificial Intelligence; Geopolymer Concrete; Climate Change; Fly Ash
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Environmental ; Engineering, Chemical
WOS记录号	WOS:001155012000001
来源期刊	JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288785
作者单位	King Fahd University of Petroleum & Minerals; King Fahd University of Petroleum & Minerals; King Fahd University of Petroleum & Minerals; King Fahd University of Petroleum & Minerals
推荐引用方式 GB/T 7714	Wudil, Y. S.,Al-Fakih, Amin,Al-Osta, Mohammed A.,et al. Intelligent optimization for modeling carbon dioxide footprint in fly ash geopolymer concrete: A novel approach for minimizing CO2 emissions[J],2024,12(1).
APA	Wudil, Y. S.,Al-Fakih, Amin,Al-Osta, Mohammed A.,&Gondal, M. A..(2024).Intelligent optimization for modeling carbon dioxide footprint in fly ash geopolymer concrete: A novel approach for minimizing CO2 emissions.JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING,12(1).
MLA	Wudil, Y. S.,et al."Intelligent optimization for modeling carbon dioxide footprint in fly ash geopolymer concrete: A novel approach for minimizing CO2 emissions".JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 12.1(2024).