基于CNN-LSTM的混凝土重力坝变形预测模型

Deformation prediction model of concrete gravity dam based on CNN-LSTM

大坝变形是评估大坝整体工作状态的重要监测指标,准确预测变形情况能够有效防范因大坝失事造成的损失。传统的预测方法基于统计或机器学习,往往难以有效捕捉混凝土坝变形与多种环境影响因子之间的复杂关系,因此提出了一种结合卷积神经网络(CNN)和长短期记忆网络(LSTM)的混凝土坝变形预测模型,即CNN-LSTM模型,并通过实际混凝土重力坝的变形监测数据验证了该模型的有效性。通过综合对比基于长短期记忆网络(LSTM)、循环神经网络(RNN)和支持向量机(SVM)的模型,发现CNN-LSTM模型在精度和泛化能力方面表现更优,可作为大坝安全监测的有效工具。

The deformation of dams is an important monitoring indicator for assessing the overall operational status of a dam.Accurate prediction of deformation can effectively prevent losses caused by dam failures.Traditional prediction methods based on statistics or machine learning often struggle to capture the complex relationships between concrete dam deformation and various environmental impact factors.To address this issue,this paper proposes a concrete dam deformation prediction model that combines Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks,namely the CNN-LSTM model.The effectiveness of this model is validated using deformation monitoring data from an actual concrete gravity dam.A comprehensive comparison with models based on LSTM,RNN,and SVM demonstrates that the proposed CNN-LSTM model excels in terms of accuracy and generalization capabilities,making it a promising tool for dam safety monitoring.