Abstract
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.
Abstract (translated)
这项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条件下的单相非饱和土体固结过程。为了应对跨多尺度时间域内的气水压力消散耦合挑战，该框架集成了对数时间分割、滞后一致性损失强制执行以及分段式迁移学习技术。在正向分析中，推荐的分割方案下LBC-PINN能够准确预测孔隙空气和孔隙水压力的变化过程。模型预测结果通过有限元方法（FEM）验证，在长达1e10秒的时间范围内平均绝对误差小于1e-2。基于特征气相消散时间的一种简化分割策略提高了计算效率，同时保持了预测准确性。敏感性分析证实该框架在空气与水渗透率比值范围从1e-3到1e3的情况下具有鲁棒性。
URL
https://arxiv.org/abs/2602.07031
