刘桓辰,朱静,郭梦京

• 1:陕西省水利电力勘测设计研究院
• 2:西安理工大学水利水电学院

摘要(Abstract)：

【目的】变形是库水、温度和材料特性等多因素耦合作用下大坝整体服役性态的直接表征，建立精确、高效的预测模型对于掌握坝体变形趋势和评估大坝风险具有重要意义。【方法】针对传统预测模型精度低、适应性差和抗噪能力弱等问题，将哈里斯鹰算法(HHO)、变分模态分解(VMD)、随机森林算法(RF)和长短时记忆神经网络(LSTM)相结合，提出了一种混凝土拱坝变形深度学习预测模型。首先，通过引入Tent混沌映射、能量随机性递减策略改进HHO算法，利用IHHO-VMD方法分解拱坝变形数据序列得到若干不同频率的模态分量(IMF);其次，利用RF算法计算变形特征因子的贡献率，筛选预测模型最优输入因子集合；最后，采用LSTM模型对各IMF分量进行学习和预测，重构各分量预测值得到最终的变形预测值。【结果】仿真信号分解结果表明：与现有信号分解方法相比，采用IHHO-VMD方法可以实现信号最优分解。通过某工程实例分析，所提模型预测4个测点位移时，平均RMSE、MAE、R~2和MAPE为0.397 6 mm、0.327 5 mm、0.991 8和1.519 4%。【结论】相较于其他组合模型，所提模型的4种评价指标结果均为最优，表明该模型具有预测精度高、泛化能力好和鲁棒性强等优势。

关键词(KeyWords)： 混凝土拱坝变形;哈里斯鹰算法;变分模态分解;随机森林算法;长短时记忆神经网络;水利工程;变形

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41807156);; 陕西省教育厅重点实验室科研计划项目(18JS073)

作者(Author): 刘桓辰,朱静,郭梦京

DOI: 10.13928/j.cnki.wrahe.2025.03.010

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