Coupling of green building construction based on particle Swarm optimizing neural network algorithm
In the continuous development of the green building industry, construction safety management faces increasing challenges, particularly in safety and environmental protection, which requires precise evaluation and control. Therefore, this study proposes a coupling analysis method for green building c...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | Sustainable Buildings |
| Subjects: | |
| Online Access: | https://www.sustainable-buildings-journal.org/articles/sbuild/full_html/2025/01/sbuild20240019/sbuild20240019.html |
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| _version_ | 1850119909357387776 |
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| author | Wang Leigang Li Shaohua Wang Liang Zhang Zheng Zhou Yuchen Chang Long |
| author_facet | Wang Leigang Li Shaohua Wang Liang Zhang Zheng Zhou Yuchen Chang Long |
| author_sort | Wang Leigang |
| collection | DOAJ |
| description | In the continuous development of the green building industry, construction safety management faces increasing challenges, particularly in safety and environmental protection, which requires precise evaluation and control. Therefore, this study proposes a coupling analysis method for green building construction based on particle swarm optimisation neural network. The purpose is to strengthen safety risk management in green building construction by combining particle swarm optimisation with neural network algorithms. A risk coupling performance comparison was conducted between traditional and research algorithms. In the results, when using a back propagation neural network for prediction, the actual construction risk rate increased from 0.235 to 0.431. the optimised algorithm showed an increase from 0.168 to 0.453, and the prediction error improving from −0.352 to 0.014, demonstrating a high degree of adaptability and accuracy to actual changes. Compared with traditional methods, the prediction error of this algorithm is significantly reduced, and the data fitting accuracy is improved to 0.99809, indicate its effectiveness in predicting construction safety risks. The research results not only contribute to improving the efficiency of safety management during the construction process, but also provide technical support for risk prediction models in the future green building field. |
| format | Article |
| id | doaj-art-0f2c4044188e4e06b67f369639dda0b2 |
| institution | OA Journals |
| issn | 2492-6035 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Sustainable Buildings |
| spelling | doaj-art-0f2c4044188e4e06b67f369639dda0b22025-08-20T02:35:32ZengEDP SciencesSustainable Buildings2492-60352025-01-018110.1051/sbuild/2025001sbuild20240019Coupling of green building construction based on particle Swarm optimizing neural network algorithmWang Leigang0Li Shaohua1Wang Liang2Zhang Zheng3Zhou Yuchen4Chang Long5Northwest Branch, China Construction Eighth Engineering Division Corp., LtdNorthwest Branch, China Construction Eighth Engineering Division Corp., LtdNorthwest Branch, China Construction Eighth Engineering Division Corp., LtdNorthwest Branch, China Construction Eighth Engineering Division Corp., LtdNorthwest Branch, China Construction Eighth Engineering Division Corp., LtdNorthwest Branch, China Construction Eighth Engineering Division Corp., LtdIn the continuous development of the green building industry, construction safety management faces increasing challenges, particularly in safety and environmental protection, which requires precise evaluation and control. Therefore, this study proposes a coupling analysis method for green building construction based on particle swarm optimisation neural network. The purpose is to strengthen safety risk management in green building construction by combining particle swarm optimisation with neural network algorithms. A risk coupling performance comparison was conducted between traditional and research algorithms. In the results, when using a back propagation neural network for prediction, the actual construction risk rate increased from 0.235 to 0.431. the optimised algorithm showed an increase from 0.168 to 0.453, and the prediction error improving from −0.352 to 0.014, demonstrating a high degree of adaptability and accuracy to actual changes. Compared with traditional methods, the prediction error of this algorithm is significantly reduced, and the data fitting accuracy is improved to 0.99809, indicate its effectiveness in predicting construction safety risks. The research results not only contribute to improving the efficiency of safety management during the construction process, but also provide technical support for risk prediction models in the future green building field.https://www.sustainable-buildings-journal.org/articles/sbuild/full_html/2025/01/sbuild20240019/sbuild20240019.htmlparticle swarm optimisationbp algorithmconstruction safetyrisk couplinggreen buildings |
| spellingShingle | Wang Leigang Li Shaohua Wang Liang Zhang Zheng Zhou Yuchen Chang Long Coupling of green building construction based on particle Swarm optimizing neural network algorithm Sustainable Buildings particle swarm optimisation bp algorithm construction safety risk coupling green buildings |
| title | Coupling of green building construction based on particle Swarm optimizing neural network algorithm |
| title_full | Coupling of green building construction based on particle Swarm optimizing neural network algorithm |
| title_fullStr | Coupling of green building construction based on particle Swarm optimizing neural network algorithm |
| title_full_unstemmed | Coupling of green building construction based on particle Swarm optimizing neural network algorithm |
| title_short | Coupling of green building construction based on particle Swarm optimizing neural network algorithm |
| title_sort | coupling of green building construction based on particle swarm optimizing neural network algorithm |
| topic | particle swarm optimisation bp algorithm construction safety risk coupling green buildings |
| url | https://www.sustainable-buildings-journal.org/articles/sbuild/full_html/2025/01/sbuild20240019/sbuild20240019.html |
| work_keys_str_mv | AT wangleigang couplingofgreenbuildingconstructionbasedonparticleswarmoptimizingneuralnetworkalgorithm AT lishaohua couplingofgreenbuildingconstructionbasedonparticleswarmoptimizingneuralnetworkalgorithm AT wangliang couplingofgreenbuildingconstructionbasedonparticleswarmoptimizingneuralnetworkalgorithm AT zhangzheng couplingofgreenbuildingconstructionbasedonparticleswarmoptimizingneuralnetworkalgorithm AT zhouyuchen couplingofgreenbuildingconstructionbasedonparticleswarmoptimizingneuralnetworkalgorithm AT changlong couplingofgreenbuildingconstructionbasedonparticleswarmoptimizingneuralnetworkalgorithm |