滞后兼容的物理信息神经网络用于非饱和土体固结分析
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
AI总结：
本文提出了一种用于非饱和土体固结分析的滞后兼容物理信息神经网络，通过多尺度时间分割和迁移学习提升预测精度与计算效率。
摘要：
本研究开发了一种滞后兼容的物理信息神经网络（LBC-PINN），用于模拟和反演长期加载下的一维非饱和土体固结。为了解决多尺度时间域中空气和水压力消散耦合的挑战，该框架集成了对数时间分割、滞后兼容性损失约束以及分段迁移学习。在正向分析中，LBC-PINN结合推荐的分割方案能够准确预测孔隙空气和孔隙水压力的演变。模型预测结果与有限元方法（FEM）结果对比，时间跨度达1e10秒时均绝对误差低于1e-2。基于特征空气相消散时间的简化分割策略在保持预测准确性的同时提高了计算效率。敏感性分析证实了该框架在空气-水渗透率比从1e-3到1e3范围内均表现出良好的鲁棒性。
英文摘要：
This study develops a Lagged Backward-Compatible Physics-Informed Neural Network (LBC-PINN) for simulating and inverting one-dimensional unsaturated soil consolidation under long-term loading. To address the challenges of coupled air and water pressure dissipation across multi-scale time domains, the framework integrates logarithmic time segmentation, lagged compatibility loss enforcement, and segment-wise transfer learning. In forward analysis, the LBC-PINN with recommended segmentation schemes accurately predicts pore air and pore water pressure evolution. Model predictions are validated against finite element method (FEM) results, with mean absolute errors below 1e-2 for time durations up to 1e10 seconds. A simplified segmentation strategy based on the characteristic air-phase dissipation time improves computational efficiency while preserving predictive accuracy. Sensitivity analyses confirm the robustness of the framework across air-to-water permeability ratios ranging from 1e-3 to 1e3.
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