Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel
Joint Authors
Li, Shuguang
Yuan, Jie
Cai, He
Liao, Taichang
Ren, Shaoqiang
Huo, Runke
Yang, Wencui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-17
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Cai, He& Liao, Taichang& Ren, Shaoqiang& Li, Shuguang& Huo, Runke& Yuan, Jie…[et al.]. 2020. Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1124920
Modern Language Association (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 No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1124920
American Medical Association (AMA)
Cai, He& Liao, Taichang& Ren, Shaoqiang& Li, Shuguang& Huo, Runke& Yuan, Jie…[et al.]. Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1124920
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124920