论文标题
物理信息的神经网络的应用用于桩土相互作用的正向和反向分析
Application of Physics-Informed Neural Networks for Forward and Inverse Analysis of Pile-Soil Interaction
论文作者
论文摘要
提出了物理知识神经网络（PINN）在桩土相互作用问题的前进和反向分析中的应用。在桩土的相互作用的人工神经网络（ANN）建模中遇到的主要挑战是材料特性突然发生变化，这导致位移溶液梯度的不连续性很大。因此，提出了一个域分解多网模型，以应对在桩土和土壤层的常见边界处的应变场中的不连续性。在轴对称和平面应变条件下，证明了模型在嵌入均匀和分层地层中嵌入的单个桩的分析和参数研究中的应用。特别研究了模型在参数识别（反向分析）中的性能。结果表明，通过使用PINN，可以成功地将沿桩长度获得的局部数据（可能是通过光纤应变感应获得）可成功地用于分层地层中土壤参数的反转。
The application of the Physics-Informed Neural Networks (PINNs) to forward and inverse analysis of pile-soil interaction problems is presented. The main challenge encountered in the Artificial Neural Network (ANN) modelling of pile-soil interaction is the presence of abrupt changes in material properties, which results in large discontinuities in the gradient of the displacement solution. Therefore, a domain-decomposition multi-network model is proposed to deal with the discontinuities in the strain fields at common boundaries of pile-soil regions and soil layers. The application of the model to the analysis and parametric study of single piles embedded in both homogeneous and layered formations is demonstrated under axisymmetric and plane strain conditions. The performance of the model in parameter identification (inverse analysis) of pile-soil interaction is particularly investigated. It is shown that by using PINNs, the localized data acquired along the pile length - possibly obtained via fiber optic strain sensing - can be successfully used for the inversion of soil parameters in layered formations.
