Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel
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 compress...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8875922 |
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| author | He Cai Taichang Liao Shaoqiang Ren Shuguang Li Runke Huo Jie Yuan Wencui Yang |
| author_facet | He Cai Taichang Liao Shaoqiang Ren Shuguang Li Runke Huo Jie Yuan Wencui Yang |
| author_sort | He Cai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-20985ec989ba4e768ab932440c559bc4 |
| institution | OA Journals |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-20985ec989ba4e768ab932440c559bc42025-08-20T02:03:55ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88759228875922Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in TunnelHe Cai0Taichang Liao1Shaoqiang Ren2Shuguang Li3Runke Huo4Jie Yuan5Wencui Yang6China Railway 20th Bureau Group Co., Ltd., Xi’an 710016, ChinaChina Railway 20th Bureau Group Co., Ltd., Xi’an 710016, ChinaChina Railway 20th Bureau Group Co., Ltd., Xi’an 710016, ChinaChina Railway 20th Bureau Group Co., Ltd., Xi’an 710016, ChinaSchool of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Transportation Science and Technology, Harbin Institute of Technology, Harbin 150090, ChinaSchool of Transportation Science and Technology, Harbin Institute of Technology, Harbin 150090, ChinaDesert 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.http://dx.doi.org/10.1155/2020/8875922 |
| spellingShingle | He Cai Taichang Liao Shaoqiang Ren Shuguang Li Runke Huo Jie Yuan Wencui Yang Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel Advances in Civil Engineering |
| title | Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel |
| title_full | Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel |
| title_fullStr | Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel |
| title_full_unstemmed | Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel |
| title_short | Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel |
| title_sort | predicting the compressive strength of desert sand concrete using ann pso and its application in tunnel |
| url | http://dx.doi.org/10.1155/2020/8875922 |
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