This work mainly focuses on determination of the fully plastic J-integral solutions for welded center cracked plates subjected to remote tension loading. Detailed three-dimensional elastic–plastic Finite Element Analyses (FEA) were implemented to compute the fully plastic J-integral along the crack front for a wide range of crack geometries, material properties and weld strength mismatch ratios for 900 cases. According to the database generated from FEA, Back-propagation Neural Network (BPNN) model was proposed to predict the values and distributions of fully plastic J-integral along crack front based on the variables used in FEA. The determination coefficient R² is greater than 0.99, indicating the robustness and goodness of fit of the developed BPNN model. The network model can accurately and efficiently predict the elastic-plastic J-integral for weld centerline crack, which can be used to perform fracture analyses and safety assessment for welded center cracked plates with varying strength mismatch conditions under uniaxial loading.

这项工作主要集中于确定承受远距离拉力载荷的焊接中心裂纹板的全塑性J积分解决方案。进行了详细的三维弹塑性有限元分析（FEA），以计算900个案例中沿裂纹前沿的全塑性J积分，适用于各种裂纹几何形状，材料特性和焊接强度不匹配率。根据有限元分析生成的数据库，基于有限元分析中使用的变量，提出了反向传播神经网络（BPNN）模型来预测沿裂纹前沿的全塑性J积分的值和分布。判定系数R ²大于0.99，表明已开发的BPNN模型的鲁棒性和拟合优度。该网络模型可以准确有效地预测焊缝中心线裂纹的弹塑性J积分，可用于在单轴载荷下对变化的强度失配条件下的焊缝中心裂纹板进行断裂分析和安全性评估。