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首页> 外文期刊>Advances in civil engineering >Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network
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Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network

机译：基于背部化神经网络的横向加固的平板柱连接的冲孔容量预测

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摘要

Punching shear failure of slab-column connections can cause the progressive collapse of a structure. In this study, a punching test database is first established. Then, based on the Levenberg-Marquardt (LM) algorithm and using the nonlinear function of the backpropagation neural network (BPNN), a prediction model of the punching capacity of slab-column connections without transverse reinforcement is established. Finally, the proposed model is compared with the formulas of the Chinese, American, and European standards using several methods. The statistical eigenvalue method shows that the BPNN model has the highest accuracy and the lowest dispersion. The defect point counting method shows that the BPNN model had the fewest total number of defects and was the safest and most economical. The influencing factor analysis suggests that factors in the BPNN model had the most reasonable influence on the punching bearing capacity of slab-column connections. Finally, the model is verified using a case study and the Matlab program. The results show that the average error of the formulas in the Chinese, American, and European standards are 21.08%, 30.21%, and 11.47%, respectively, higher than that of the BPNN model.
机译：平板柱连接的冲压剪切失效可能导致结构的逐渐崩溃。在本研究中，首先建立一个冲压测试数据库。然后，基于Levenberg-Marquardt（LM）算法并使用Backpropagation神经网络（BPNN）的非线性函数，建立了没有横向增强的平板柱连接的冲孔容量的预测模型。最后，使用几种方法将拟议的模型与中国，美国和欧洲标准的公式进行比较。统计特征值方法表明，BPNN模型具有最高的精度和最低色散。缺陷点计数方法表明，BPNN模型具有最少的缺陷数，最安全，最经济。影响因素分析表明，BPNN模型中的因素对平板连接的冲孔容量具有最合理的影响。最后，使用案例研究和MATLAB程序来验证该模型。结果表明，中国，美国和欧洲标准中公式的平均误差分别比BPNN模型的21.08％，30.21％和11.47％。

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  • 来源
    《Advances in civil engineering》 |2019年第16期|7904685.1-7904685.19|共19页
  • 作者

    Bu Jie; Zhang Fanzhen; Zhu Meng; He Zhiyang; Hu Qigao;

  • 作者单位

    Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410072 Peoples R China;

    Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410072 Peoples R China;

    Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410072 Peoples R China;

    Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410072 Peoples R China;

    Natl Univ Def Technol Coll Mil Educ & Training Changsha 410072 Peoples R China;

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