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DOI	10.12989/scs.2024.50.4.443
	Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete
	Bi, Ying; Yi, Yeng
发表日期	2024
ISSN	1229-9367
EISSN	1598-6233
起始页码	50
结束页码	4
卷号	50 期号:4
英文摘要	The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO2 emissions in the building industry. In the present work, the compressive strength (��) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast -furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R2 metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics a 50% for CRFR to ARFR.
英文关键词	compressive strength; geopolymer concrete; natural zeolite; random forests regression; silica fume
语种	英语
WOS研究方向	Construction & Building Technology ; Engineering ; Materials Science
WOS类目	Construction & Building Technology ; Engineering, Civil ; Materials Science, Composites
WOS记录号	WOS:001205402000007
来源期刊	STEEL AND COMPOSITE STRUCTURES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299776
推荐引用方式 GB/T 7714	Bi, Ying,Yi, Yeng. Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete[J],2024,50(4).
APA	Bi, Ying,&Yi, Yeng.(2024).Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete.STEEL AND COMPOSITE STRUCTURES,50(4).
MLA	Bi, Ying,et al."Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete".STEEL AND COMPOSITE STRUCTURES 50.4(2024).