基于改進BP神經(jīng)網(wǎng)絡(luò)的T形圓管節(jié)點應(yīng)力集中系數(shù)研究

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中圖分類號：TU391 文獻標志碼：A

本文引用格式：，等.基于改進BP神經(jīng)網(wǎng)絡(luò)的T形圓管節(jié)點應(yīng)力集中系數(shù)研究[J].華東交通大學(xué)學(xué)報，2025，42（3）：108-116.

Investigation on Stress Concentration Factor ofT-tubular Joints Based on Improved BPNeural Network

ZhouYunlai'，ChenJifeng'，WangYubo1，YangQiang2，YaoFeng （1.StateKeyLaboratoryforStrengthandVibrationofMechanicalStructures，SchoolofAerospaceEngiering，Xi'anJiaotong University，Xi'an7o049，China;2.NationalKeyLaboratoryofStrengthandtructural IntegrityAircraftStrength Research Institute of China， Xi'an 710065， China）

Abstract： The BP neural network improved by the dung beetle optimization（DBO） is used to calculate the stress concentration factor （SCF） of T-tubular joints，and the SCF canbe solved quickly and accurately.First， finite element parameterized modeling ofT-tubular joints under basic axial loading was conducted，and comparative analysis with experimental data verified the model's reliability.Next，a SCFdataset was established for crown and saddle points，analyzing the influence of dimensionless geometric parameters on SCF.Finally，the BP neural network improved by DBO is used to perform regression prediction on the SCF data sets of joints with different geometric parameters.The results show that the prediction performance of the improved BP neural network model is better than that of the unimproved BP neural network. Compared with the SCF parameter equation， the BP neural network prediction using DBO is more efficient and accurate.

KeyWords：T-tubular joints; BP neural network; hot spot stress; stressconcentration factor; dung beetle optimization

Citation format： ZHOU YL， CHENJF， WANG Y B，et al. Investigation on stress concentration factor of Ttubular joints based on improved BP neural network[J]. Journal of East China Jiaotong University，2O25，42（3）： 108-116.

圓鋼管節(jié)點以其卓越的力學(xué)性能和建筑特性，被廣泛用于海洋平臺、航站樓和體育場館等大跨度建筑中，其中T形圓管節(jié)點是常見的結(jié)構(gòu)形式之一。（剩余10895字）