一种双图时空图神经网络用于四点弯曲条件下钢筋混凝土梁非线性响应预测的替代模型
A Dual-Graph Spatiotemporal GNN Surrogate for Nonlinear Response Prediction of Reinforced Concrete Beams under Four-Point Bending
AI总结：
本文提出了一种双图时空图神经网络模型，用于预测四点弯曲条件下钢筋混凝土梁的非线性响应，通过结合节点和元素级的动力学，提高预测精度和效率。
摘要：
高性能的非线性有限元（FE）模拟仍然成本高昂，尤其是在参数设置中加载位置变化时。我们开发了一种双图时空图神经网络（GNN）替代模型，用于近似四点弯曲条件下钢筋混凝土（RC）梁的时间历程。为了生成训练数据，我们运行了一个参数化的Abaqus任务，独立地在网格对齐的网格上移动两个加载块，并在固定归一化加载水平下导出全场响应。该模型以自回归的方式进行，联合预测节点位移、元素级的von Mises应力、元素级等效塑性应变（PEEQ）以及全局垂直反力。一个关键动机是当元素数量被强制通过节点表示时引入的峰值损失。因此，我们通过两个递归图分支耦合节点级和元素级的动力学：一个节点级图卷积门控递归单元（GConvGRU）用于运动学，一个元素级GConvGRU用于历史依赖的内部变量，通过在元素分支上进行池化来预测全局反力。在受控的消融实验中，移除元素到节点到元素路径可以提高局部高梯度应力/PEEQ区域的峰值敏感预测，而不会影响全局载荷位移趋势。训练后，该替代模型以非线性FE成本的很小一部分生成完整的轨迹，从而加速参数评估和设计探索。
英文摘要：
High-fidelity nonlinear finite-element (FE) simulations of reinforced-concrete (RC) structures are still costly, especially in parametric settings where loading positions vary. We develop a dual-graph spatiotemporal GNN surrogate to approximate the time histories of RC beams under four-point bending. To generate training data, we run a parametric Abaqus campaign that independently shifts the two loading blocks on a mesh-aligned grid and exports full-field responses at fixed normalized loading levels. The model rolls out autoregressively and jointly predicts nodal displacements, element wise von Mises stress, element-wise equivalent plastic strain (PEEQ), and the global vertical reaction force in a single multi-task setup. A key motivation is the peak loss introduced when element quantities are forced through node-based representations. We therefore couple node- and element-level dynamics using two recurrent graph branches: a node-level graph convolutional gated recurrent unit (GConvGRU) for kinematics and an element-level GConvGRU for history-dependent internal variables, with global force predicted through pooling on the element branch. In controlled ablations, removing the Element to Node to Element pathway improves peak-sensitive prediction in localized high-gradient stress/PEEQ regions without degrading global load displacement trends. After training, the surrogate produces full trajectories at a fraction of the cost of nonlinear FE, enabling faster parametric evaluation and design exploration.
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