基于HST-NN集成模型的特高拱坝首次蓄水期变形预测研究

Deformation prediction of a super-high arch dam during initial impoundment based on the HST-NN integrated model

摘要

针对超高拱坝首次蓄水期过程中数据量动态变化大、环境因素复杂等问题，传统模型在数据不足时预测稳定性差、在数据丰富时又难以充分挖掘非线性规律，亟须构建一种兼具机理约束与数据驱动能力的高适应性预测模型。提出一种HST-NN集成模型，融合HST公式模型的物理机理约束与深度神经网络(DNN)的非线性特征学习能力，适应蓄水全过程中数据量从稀缺到充足的动态变化。基于某300 m级超高拱坝首次蓄水期实测数据，开展模型构建与分阶段验证分析。结果表明，该模型在数据稀缺阶段相对误差控制在10%以内，数据丰富阶段进一步降至5%以下，相较传统单一模型预测精度和稳定性显著提升。HST-NN模型具备良好的自适应性与泛化能力，可为超高拱坝首次蓄水期变形预测与安全运行提供有效技术支撑。

Abstract

During the initial impoundment period of super-high arch dams, the monitoring data are characterized by dynamic variation and complex environmental influences. Traditional models often suffer from poor prediction stability under data-scarce conditions and insufficient capability to capture nonlinear patterns when data are abundant. Therefore, there is an urgent need to establish a high-adaptability prediction model that integrates physical constraints and data-driven capabilities. This paper proposes an HST-NN integrated model, which combines the physical mechanism constraints of the HST(Hydrostatic-Seasonal-Time) formula model with the nonlinear feature learning ability of a Deep Neural Network(DNN), to accommodate the evolving data conditions throughout the entire impoundment process. The model is constructed and validated in stages based on measured deformation data from the initial impoundment of a 300 m-class super-high arch dam.Results show that the proposed model maintains a relative error within 10% under data-scarce conditions and further reduces to below 5% as data increases. Compared to traditional single models, the HST-NN model significantly improves prediction accuracy and stability. It demonstrates strong adaptability and generalization, providing an effective tool for deformation prediction and safety management during the initial impoundment of super-high arch dams.

关键词

超高拱坝 / 首次蓄水期 / 变形预测 / HST-NN模型 / 深度学习

Key words

super-high arch dam / initial impoundment / deformation prediction / HST-NN model / deep learning

基金资助

中国长江电力股份有限公司资助项目(Z242302042)