用多分辨率时序模型集成，减少挡土墙变形预测误差累积。
Spatio-Temporal Forecasting of Retaining Wall Deformation: Mitigating Error Accumulation via Multi-Resolution ConvLSTM Stacking Ensemble
- 构建多分辨率输入的ConvLSTM集成模型，融合不同时间尺度信息。
- 在2000条模拟与实测数据上验证，长时预测误差降低，稳定性提升。
- 适合需要高精度长期预测的岩土工程智能监测场景。
本研究提出一种多分辨率卷积长短期记忆（ConvLSTM）集成框架，通过利用不同时间输入分辨率来缓解分阶段开挖过程中挡土结构变形的长期预测误差累积问题。基于PLAXIS2D仿真生成了包含五层土层、两种开挖深度（14米和20米）及随机变化的岩土与结构参数的广义数据库，得到2000条时序位移曲线。三个在不同输入分辨率下训练的ConvLSTM模型，通过全连接神经网络元学习器进行集成。使用数值结果与现场观测数据验证表明，该集成方法在长时多步预测中持续优于单个ConvLSTM模型，表现出更少的误差传播和更强的泛化能力。研究证实，联合利用多种时间尺度的多分辨率集成策略，可显著提升人工智能驱动的岩土工程预测中的稳定性与准确性。
原文摘要 · Abstract (English)
This study proposes a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) ensemble framework that leverages diverse temporal input resolutions to mitigate error accumulation and improve long-horizon forecasting of retaining-structure behavior during staged excavation. An extensive database of lateral wall displacement responses was generated through PLAXIS2D simulations incorporating five-layered soil stratigraphy, two excavation depths (14 and 20 m), and stochastically varied geotechnical and structural parameters, yielding 2,000 time-series deflection profiles. Three ConvLSTM models trained at different input resolutions were integrated using a fully connected neural network meta-learner to construct the ensemble model. Validation using both numerical results and field measurements demonstrated that the ensemble approach consistently outperformed the standalone ConvLSTM models, particularly in long-term multi-step prediction, exhibiting reduced error propagation and improved generalization. These findings underscore the potential of multi-resolution ensemble strategies that jointly exploit diverse temporal input scales to enhance predictive stability and accuracy in AI-driven geotechnical forecasting.
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