Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete
In order to achieve resource conservation, protect the environment and realize the sustainable development of the construction industry, the low energy resource utilization of construction waste was explored. In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, w...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2908616 |
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| _version_ | 1832565449490956288 |
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| author | Hongyang Xie Jianjun Dong Yong Deng Yiwen Dai |
| author_facet | Hongyang Xie Jianjun Dong Yong Deng Yiwen Dai |
| author_sort | Hongyang Xie |
| collection | DOAJ |
| description | In order to achieve resource conservation, protect the environment and realize the sustainable development of the construction industry, the low energy resource utilization of construction waste was explored. In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, water to material ratio, and HPMC content on the physical and mechanical properties of recycled brick powder foam concrete was investigated by conducting a 4-factor, 5-level orthogonal test with recycled brick powder as fine aggregate, and the effect of each factor on the physical and mechanical properties of recycled brick powder foam concrete was derived, and the optimum ratio of recycled brick powder foam concrete was determined by analysing the specific strength. Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO-BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO-BP model was found to have obvious advantages in terms of prediction accuracy and model stability. The experimental results and prediction models can provide experimental and theoretical references for the research and application of recycled brick powder foam concrete. |
| format | Article |
| id | doaj-art-6a396d7bad48485a9bcb3bc11bc00854 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-6a396d7bad48485a9bcb3bc11bc008542025-02-03T01:07:47ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/2908616Research and Model Prediction on the Performance of Recycled Brick Powder Foam ConcreteHongyang Xie0Jianjun Dong1Yong Deng2Yiwen Dai3College of Civil Engineering and ArchitectureCollege of Civil Engineering and ArchitectureCollege of Civil Engineering and ArchitectureCollege of Civil Engineering and ArchitectureIn order to achieve resource conservation, protect the environment and realize the sustainable development of the construction industry, the low energy resource utilization of construction waste was explored. In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, water to material ratio, and HPMC content on the physical and mechanical properties of recycled brick powder foam concrete was investigated by conducting a 4-factor, 5-level orthogonal test with recycled brick powder as fine aggregate, and the effect of each factor on the physical and mechanical properties of recycled brick powder foam concrete was derived, and the optimum ratio of recycled brick powder foam concrete was determined by analysing the specific strength. Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO-BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO-BP model was found to have obvious advantages in terms of prediction accuracy and model stability. The experimental results and prediction models can provide experimental and theoretical references for the research and application of recycled brick powder foam concrete.http://dx.doi.org/10.1155/2022/2908616 |
| spellingShingle | Hongyang Xie Jianjun Dong Yong Deng Yiwen Dai Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete Advances in Civil Engineering |
| title | Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete |
| title_full | Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete |
| title_fullStr | Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete |
| title_full_unstemmed | Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete |
| title_short | Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete |
| title_sort | research and model prediction on the performance of recycled brick powder foam concrete |
| url | http://dx.doi.org/10.1155/2022/2908616 |
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