用多分辨率LSTM模型预测挡土墙变形，实测误差仅1.4毫米。
Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction
- 融合多时间尺度的ConvLSTM，通过集成学习提升预测能力。
- 在韩国11个工地34个倾角仪数据上，平均误差1.4毫米，决定系数达0.93。
- 仅用仿真数据训练，却能准确预测真实工地变形，适合工程安全监测。
本研究对一种用于分阶段开挖期间挡土墙变形预测的多分辨率卷积长短期记忆（ConvLSTM）框架进行了全面的现场验证。该框架基于添加高斯噪声的数值模拟数据训练，并通过堆叠集成策略整合不同时间分辨率的ConvLSTM模型。使用韩国11个开挖场地共34个倾角仪的现场监测数据进行验证，采用多种评估指标系统评估各场地预测性能，并分析了时间变形不规则性及时空预测特性对模型表现的影响。结果表明，该框架可预测最多5.0米额外开挖引起的挡土墙变形，跨所有场地平均绝对误差为1.4毫米，决定系数达0.93。这说明尽管模型仅在数值模拟与增强数据库上训练，仍能有效应用于多样化的现场开挖条件，在实际挡土墙变形预测中达到可靠的精度水平。
原文摘要 · Abstract (English)
This study presents a comprehensive field validation of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting retaining wall deformation during staged excavation. The framework is trained on Gaussian noise-augmented numerical simulations and integrates ConvLSTM models operating at different temporal resolutions through a stacking ensemble strategy. The proposed framework is validated using field monitoring data from 34 inclinometers across 11 excavation sites in South Korea. Site-wise prediction performance is systematically evaluated using multiple evaluation metrics, with analyses of the influence of temporal deformation irregularity and spatiotemporal prediction characteristics on model performance. The results demonstrate that the framework predicts retaining wall deformation associated with up to 5.0 m of additional excavation with an average mean absolute error of 1.4 mm and a coefficient of determination of 0.93 across the excavation sites. These results indicate that the framework, although trained exclusively on numerically simulated and augmented database, can be effectively applied to diverse field excavation conditions and achieve a reliable level of prediction accuracy in practical retaining wall deformation prediction.
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