摘要
针对既有输电铁塔的加固需求,采用BP神经网络算法,对输电铁塔双角钢组合截面构件的极限承载力进行了预测分析,并与行业标准进行对比。结果表明,基于BP神经网络的模型预测精度高于行业标准的预测结果,可较准确预测输电铁塔加固构件的极限承载力。
In allusion to the reinforcement demand of existing transmission tower,the BP neural network algorithm is adopted to predict and analyze the ultimate bearing capacity of the double-angle steel combined cross-section components of the transmission tower,and compare with the industry standard.The results show that the prediction precision of the model based on BP neural network is higher than that of the industry standard,and the ultimate bearing capacity of the reinforcement components of transmission tower can be predicted accurately.
作者
辛振科
魏欢欢
XIN Zhenke;WEI Huanhuan(Gansu Institute of Water and Hydropower Engineering Investigation Design and Research Co.,Ltd.,Lanzhou 730000,China;Xi’an University of Technology,Xi'an 710048,China;Yangling Vocational&Technical College,Xianyang 712100,China)
出处
《山东电力高等专科学校学报》
2021年第6期6-9,13,共5页
Journal of Shandong Electric Power College
关键词
输电铁塔
加固
极限承载力
BP神经网络
双角钢
组合截面
transmission tower
reinforcement
ultimate bearing capacity
BP neural network
double-angle steel
combined cross-section
