| Zhao Guangzhe (赵光哲)*,Yang Hanting*,Tu Bing**,Zhou Meiling**,Zhou Chengle**.[J].高技术通讯(英文),2020,26(1):92~97 |
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| Structural form selection of the high-rise building with the improved BP neural network |
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| DOI：doi：10.3772/j.issn.1006-6748.2020.01.012 |
| 中文关键词: |
| 英文关键词: back propagation (BP) neural network, high-rise building, structural form selection, Levenberg-Marquardt (L-M) algorithm |
| 基金项目: |
| Author Name | Affiliation | | Zhao Guangzhe (赵光哲)* | (*School of Electrical and Information Engineering， Beijing University of Civil Engineering
and Architecture, Beijing 100044, P.R.China) | | Yang Hanting* | (*School of Electrical and Information Engineering， Beijing University of Civil Engineering
and Architecture, Beijing 100044, P.R.China) | | Tu Bing** | (**College of Information and Communication Engineering， Hunan Institute of Science and Technology, Yueyang 414000, P.R.China) | | Zhou Meiling** | (**College of Information and Communication Engineering， Hunan Institute of Science and Technology, Yueyang 414000, P.R.China) | | Zhou Chengle** | (**College of Information and Communication Engineering， Hunan Institute of Science and Technology, Yueyang 414000, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| As civil engineering technology development, the structural form selection is more and more critical in design of high-rise buildings. However, structural form selection involves expertise knowledge and changes with the environment which makes the task arduous. An approach utilizing improved back propagation (BP) neural network optimized by the Levenberg-Marquardt (L-M) algorithm is proposed to extract the main controlling factors of structural form selection. Then, an intelligent expert system with artificial neural network is constructed to design high-rise buildings structure effectively.The experiment tests the model in 15 well-known architecture samples and get the prediction accuracy of 93.33%. The results show that the method is feasible and can help designers select the appropriate structural form. |
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