干旱区生态环境知识资源中心(Arid): Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel

Arid

DOI	10.1155/2020/8875922
	Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel
	Cai, He; Liao, Taichang; Ren, Shaoqiang; Li, Shuguang; Huo, Runke; Yuan, Jie; Yang, Wencui
通讯作者	Li, SG (corresponding author), China Railway 20th Bur Grp Co Ltd, Xian 710016, Peoples R China. ; Li, SG (corresponding author), Xian Univ Architecture & Technol, Sch Civil Engn, Xian 710055, Peoples R China.
来源期刊	ADVANCES IN CIVIL ENGINEERING
ISSN	1687-8086
EISSN	1687-8094
出版年	2020
卷号	2020
英文摘要	Desert sand is one of the current research hotspots in alternative materials for concrete aggregates. In the process of practical application, compressive strength is an essential prerequisite for studying other properties. Based on the current research situation, a prediction technology of compressive strength of desert sand concrete (DSC) is proposed based on an artificial neural network (ANN) and a particle swarm optimization (PSO). The technology is a prediction model that adjusts the network architecture by using the PSO method based on the ANN optimization model. Water-binder ratio, sand ratio, replacement rate of desert sand, desert sand type, fly ash content, silica fume content, air content, and slump were selected as the neural network's inputs. The compressive strength data of 118 different combinations of influencing variables were tested to establish the dataset. The results show that the PSO method is efficient for the ANN in DSC compressive strength research. Furthermore, referring to this method, DSC is applied to the shotcrete of tunnels in construction engineering successfully, and the dust particle content during construction is evaluated.
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000603612500003
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; HIGH-PERFORMANCE CONCRETE ; MECHANICAL-PROPERTIES ; MIX PROPORTION ; DESERTIFICATION ; OPTIMIZATION ; AGGREGATE ; ALGORITHM ; SILICA ; FACE
WOS类目	Construction & Building Technology ; Engineering, Civil
WOS研究方向	Construction & Building Technology ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/348685
作者单位	[Cai, He; Liao, Taichang; Ren, Shaoqiang; Li, Shuguang] China Railway 20th Bur Grp Co Ltd, Xian 710016, Peoples R China; [Li, Shuguang; Huo, Runke] Xian Univ Architecture & Technol, Sch Civil Engn, Xian 710055, Peoples R China; [Yuan, Jie; Yang, Wencui] Harbin Inst Technol, Sch Transportat Sci & Technol, Harbin 150090, Peoples R China
推荐引用方式 GB/T 7714	Cai, He,Liao, Taichang,Ren, Shaoqiang,et al. Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel[J],2020,2020.
APA	Cai, He.,Liao, Taichang.,Ren, Shaoqiang.,Li, Shuguang.,Huo, Runke.,...&Yang, Wencui.(2020).Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel.ADVANCES IN CIVIL ENGINEERING,2020.
MLA	Cai, He,et al."Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel".ADVANCES IN CIVIL ENGINEERING 2020(2020).