| 陈旭东,蓝婷婷,胡少伟,徐颖,郭进军,顾冲时.考虑库水位和温度变化滞后效应的混凝土坝渗流预测模型研究[J].水利学报,2025,56(7):862-873 |
| 考虑库水位和温度变化滞后效应的混凝土坝渗流预测模型研究 |
| Research on the predictive model for seepage in concrete dams considering the hysteresis effects of reservoir water level and temperature variations |
| 投稿时间：2024-05-12 修订日期：2025-02-20 |
| DOI：10.13243/j.cnki.slxb.20240276 |
| 中文关键词: 混凝土坝 渗流性态 滞后效应 AM-SSA-BiGRU预测模型 贝叶斯向量自回归 |
| 英文关键词: concrete dams seepage behavior hysteresis effect AM-SSA-BiGRU prediction model Bayesian vector autoregression |
| 基金项目:国家重点研发计划项目(2022YFC3004402); 国家自然科学基金项目(U2040224); 河南省青年骨干教师培养计划(2024GGJS007); 河南省自然科学基金项目(232300421194); “一带一路”水与可持续发展基金项目(U2021nkms06); 国家大坝安全工程技术研究中心基金项目(CX2022B05) |
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| 中文摘要: |
| 渗流性态是库水位、温度等外部环境荷载和内部防渗排水构造等交互作用的综合反映，而库水位和温度变化对渗流影响的滞后效应，目前尚无有效量化方法。本文旨在探明该滞后效应规律，构建相应的量化表达式，据此建立渗流预测模型。首先，采用贝叶斯向量自回归模型（BVAR）分析库水位和温度对渗流影响的滞后过程，量化表示库水位和温度影响分量。其次，为有效表征渗流与影响因素间的非线性映射关系，利用注意力机制（AM）动态调整渗流输入因子的影响权重，加强双向门控循环单元（BiGRU）对关键信息的筛选能力，并引入麻雀搜索算法（SSA）提升全局搜索和自适应性能，建立了混凝土坝渗流预测AM-SSA-BiGRU模型。实例研究表明：BVAR方法能够反映库水位和温度对渗流影响的滞后过程。本文模型有效捕捉了渗流变化趋势，具有较高精度和良好的鲁棒性。研究可为深入理解混凝土坝渗流性态演变及其性能预测提供新的手段。 |
| 英文摘要: |
| Seepage behavior is a comprehensive reflection of the interaction between external environmental loads,such as reservoir water level and temperature,and the internal anti-seepage and drainage structures.However,there is no effective qualification method at present for the hysteresis effect of reservoir water level and temperature change on seepage.This study aims to explore the law of hysteresis effect,develop a quantitative expression of the hysteresis effect,and establish a seepage prediction model accordingly.The Bayesian Vector Autoregression （BVAR） model was firstly used to analyze the hysteresis process of reservoir water level and temperature on seepage flow,quantitatively representing the components of reservoir water level and temperature influence.Secondly,to effectively characterize the non-linear mapping relationship between seepage and influencing factors,the Attention Mechanism （AM） was used to dynamically adjust influence weights of seepage input factors,and the Bidirectional Gated Recurrent Unit （BiGRU） was strengthened to screen key information.The Sparrow Search Algorithm （SSA） was introduced to improve global search and adaptive performance,establishing AM-SSA-BiGRU model for seepage prediction of concrete dams.The case study demonstrates that the BVAR method can reflect the hysteresis process of reservoir water level and temperature effects on seepage.The AM-SSA-BiGRU prediction model effectively captures the seepage trend with high accuracy and robustness,which provides a novel approach for a deeper understanding of the evolution of seepage patterns and performance prediction of concrete dams. |
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