关键词: 缺口圆棒拉伸试验, 应力应变本构关系, 贝叶斯正则化反向传播神经网络, 灰狼优化算法, 优化反演

Abstract: As a key weak link of oil and gas pipelines， the girth weld of high-grade steel pipeline was always concerned by the engineering and scientific research communities.  As a typical welding structure， the welds had obvious heterogeneity， which led to the inability to accurately test the axial mechanics properties of girth weld materials， which seriously affected the accuracy of safety evaluation of pipeline girth welds.  Based on MATLAB-PYTHON-ABAQUS co-simulation， an optimization inversion method of the material stress-strain constitutive relationship was proposed in the weld zones for the high-grade steel pipelines.  Four groups of uniaxial tensile tests with different notch sizes were carried out， and the load-displacement curves of each sample were obtained.  The true stress-strain constitutive relationship of the weld zone materials was obtained by BRBP neural network and GWO， and the accuracy of the constitutive relationship was fully verified by the testing data， the results show that the relative error is less than 1%.  The inversion method proposed is also suitable for the determination of stress-strain curves of homogeneous metal materials in large strain ranges.  The inversion method may provide accurate stress-strain constitutive relationship and strength matching relationship for the safety evaluation of girth welds of high-grade steel pipelines， and further ensure the safe operation of oil and gas pipelines. 

Key words: notched bar tensile test, stress-strain constitutive relationship, Bayesian regularization back propagation（BRBP） neural network, grey wolf optimizer（GWO）, optimization inversion