新型法兰连接模块化装配式网壳结构稳定承载力智能化预测

张爱林; 冯欢; 姜子钦; 刘怡

doi:10.3724/j.gyjzG24031604

新型法兰连接模块化装配式网壳结构稳定承载力智能化预测

doi: 10.3724/j.gyjzG24031604

张爱林^1,2,3,
冯欢¹,
姜子钦^1,2, ,,
刘怡¹

1. 北京工业大学建筑工程学院, 北京 100124;
2. 北京工业大学, 北京市高层和大跨度预应力钢结构工程技术研究中心, 北京 100124;
3. 北京建筑大学北京未来城市设计高精尖创新中心, 北京 100044

基金项目:

国家自然科学基金项目(52130809)。

详细信息

作者简介:
张爱林,教授,博士生导师,主要从事装配式钢结构方面的研究,zhangal@bjut.edu.cn。

通讯作者:
姜子钦,教授,博士生导师,主要从事装配式钢结构方面的研究,jzqbj2010@163.com。

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出版历程
- 收稿日期: 2024-03-16
- 网络出版日期: 2024-09-19

Intelligent Prediction of Stability Bearing Capacity of New-Type Modular Assembled Latticed Shells with Flange Connections

ZHANG Ailin^1,2,3,
FENG Huan¹,
JIANG Ziqin^{1,2
, ,},
LIU Yi¹

1. College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Engineering Research Center of High-Rise and Large-Span Prestressed Steel Structure, Beijing University of Technology, Beijing 100124, China;
3. Beijing Advanced Innovation Center for Future City Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

摘要

摘要: 模块化装配式网壳结构具有施工效率高、节点质量好等优点,其具有广泛的应用前景。基于机器学习方法,建立了新型法兰节点模块化装配式网壳结构的稳定承载力智能化预测模型。首先,针对影响稳定极限承载力的一系列参数,分析了864个模块化装配式网壳的有限元模型,从而生成机器学习算法所需的数据库。其次,基于开源平台Scikit-learn建立了6种机器学习算法模型,使用生成的数据库对所有算法模型进行训练和测试,并且对深度神经网络模型(ANN)、XGBoost和梯度增强算法模型进行了过拟合检验,对ANN模型进行了鲁棒性和可靠性检验。结果表明,在测试集中ANN、XGBoost和梯度增强算法模型预测极限承载力的决定系数(R²)均大于0.95,预测稳定承载力的精度很高。采用ANN模型预测稳定承载力的鲁棒性和精度均最好,平均绝对百分比误差(MAPE)为7.1%,R²为0.982,具有很高的预测准确性和泛化能力,能够很好地捕捉稳定承载力和输入参数之间的复杂映射关系。
- 网壳结构 /
- 模块化装配 /
- 机器学习 /
- 承载力智能化预测 /
- 稳定承载力
Abstract: Modular assembled reticulated shell structures have the advantages of high construction efficiency and good join quality, and have a wide application prospect. Based on machine learning method, an intelligent prediction model for the bearing capacity of modular prefabricated reticulated shell structures was established. Firstly, 864 finite element models of modular assembled reticulated shells were analyzed for a series of parameters that affect the stable ultimate bearing capacity, thus generating the database required for the machine learning algorithm. Secondly, six machine learning algorithm models were established based on the open source platform Scikit-learn, and all algorithm models were trained and tested by using the generated database. In addition, the artificial neural network model (ANN), XGBoost and gradient enhancement algorithm models were overfitted, and the reliability of ANN model was tested. The results showed that the determination coefficients (R²) of ANN, XGBoost and gradient enhancement algorithm models in the test set were all greater than 0.95, and the prediction accuracy of bearing capacity was very high. The ANN model had the best robustness and accuracy in predicting the stable ultimate bearing capacity, with an average absolute percentage error (MAPE) of 7.1% and an R² of 0.982. It showed high prediction accuracy and generalization capacity, and could well capture the complex mapping relations between the ultimate bearing capacity and input parameters.
- reticulated shell structure /
- modular assembly /
- machine learning /
- intelligent prediction of bearing capacity /
- stability bearing capacity

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