Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction
An orthogonal test method was used to do sensibility analysis on the compressive strength and splitting strength of hybrid fiber-reinforced recycled aggregate concrete (HyFRAC). And a prediction model of compressive strength of HyFRAC based on Convolutional Neural Network (CNN) was proposed. The res...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-05-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2021.1918553 |
| _version_ | 1818200620171001856 |
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| author | Wei Huang Wenli Quan Pei Ge |
| author_facet | Wei Huang Wenli Quan Pei Ge |
| author_sort | Wei Huang |
| collection | DOAJ |
| description | An orthogonal test method was used to do sensibility analysis on the compressive strength and splitting strength of hybrid fiber-reinforced recycled aggregate concrete (HyFRAC). And a prediction model of compressive strength of HyFRAC based on Convolutional Neural Network (CNN) was proposed. The results show the ratio of recycled brick aggregate (RBA) to recycled concrete aggregate (RCA) has been proved the greatest influence on the compressive strength and splitting tensile strength of HyFRAC, followed by the water reducing agent content, finally the ratio of glass fiber (GF) to polypropylene fiber (PF). When RBA/RCA = 2/8, GF/PF = 7/3, and water reducing agent content is 0%, the compressive strength and splitting tensile strength of HyFRAC are the highest. According to JGJ/T10-2011, when RBA/RCA ≤ 6/4 and water reducing agent content ≥ 0.4%, the HyFRAC slump meets the 50m pumping height requirement; when RBA/RCA ≤ 4/6 and water reducing agent content ≥ 0.6%, the HyFRAC slump meets the 100m pumping height requirement. Compared to back propagation (BP) neural network model and multiple linear regression model, CNN model is more efficient in estimating the compressive strength of HyFRAC. The average relative errors and max relative errors of CNN model are 1.98% and 4.12%, respectively. |
| first_indexed | 2024-12-12T02:40:33Z |
| format | Article |
| id | doaj.art-f371afe19572461c8c877f53a3457a71 |
| institution | Directory Open Access Journal |
| issn | 1347-2852 |
| language | English |
| last_indexed | 2024-12-12T02:40:33Z |
| publishDate | 2022-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj.art-f371afe19572461c8c877f53a3457a712022-12-22T00:41:11ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522022-05-01213986100110.1080/13467581.2021.19185531918553Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network predictionWei Huang0Wenli Quan1Pei Ge2Xi’an University of Architecture & TechnologyXi’an University of Architecture & TechnologyXi’an University of Architecture & TechnologyAn orthogonal test method was used to do sensibility analysis on the compressive strength and splitting strength of hybrid fiber-reinforced recycled aggregate concrete (HyFRAC). And a prediction model of compressive strength of HyFRAC based on Convolutional Neural Network (CNN) was proposed. The results show the ratio of recycled brick aggregate (RBA) to recycled concrete aggregate (RCA) has been proved the greatest influence on the compressive strength and splitting tensile strength of HyFRAC, followed by the water reducing agent content, finally the ratio of glass fiber (GF) to polypropylene fiber (PF). When RBA/RCA = 2/8, GF/PF = 7/3, and water reducing agent content is 0%, the compressive strength and splitting tensile strength of HyFRAC are the highest. According to JGJ/T10-2011, when RBA/RCA ≤ 6/4 and water reducing agent content ≥ 0.4%, the HyFRAC slump meets the 50m pumping height requirement; when RBA/RCA ≤ 4/6 and water reducing agent content ≥ 0.6%, the HyFRAC slump meets the 100m pumping height requirement. Compared to back propagation (BP) neural network model and multiple linear regression model, CNN model is more efficient in estimating the compressive strength of HyFRAC. The average relative errors and max relative errors of CNN model are 1.98% and 4.12%, respectively.http://dx.doi.org/10.1080/13467581.2021.1918553recycled brick aggregaterecycled concrete aggregatehybrid fiberorthogonal testconvolutional neural networkcompressive strengthsplit tensile strength |
| spellingShingle | Wei Huang Wenli Quan Pei Ge Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction Journal of Asian Architecture and Building Engineering recycled brick aggregate recycled concrete aggregate hybrid fiber orthogonal test convolutional neural network compressive strength split tensile strength |
| title | Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction |
| title_full | Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction |
| title_fullStr | Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction |
| title_full_unstemmed | Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction |
| title_short | Orthogonal tests investigation into hybrid fiber-reinforce recycled aggregate concrete and convolutional neural network prediction |
| title_sort | orthogonal tests investigation into hybrid fiber reinforce recycled aggregate concrete and convolutional neural network prediction |
| topic | recycled brick aggregate recycled concrete aggregate hybrid fiber orthogonal test convolutional neural network compressive strength split tensile strength |
| url | http://dx.doi.org/10.1080/13467581.2021.1918553 |
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