Cao Xin, Sheng Jinbao, Jiang Chao, Yuan Dongyang, Zhang Hongrui
Nanjing Hydraulic Research Institute, Nanjing, 210029, Jiangsu, China.
Dam Safety Management Center of the Ministry of Water Resources, Nanjing, 210029, Jiangsu, China.
Sci Rep. 2025 Mar 12;15(1):8458. doi: 10.1038/s41598-025-92806-y.
Concrete dam structures respond to various influencing factors with complex nonlinear characteristics and notable time lags. Deformation serves as a crucial monitoring metric, providing a direct indication of the structural response of these dams. An effective deformation analysis and prediction model is essential for accurately assessing the health of concrete dam structures. Current deformation prediction models have limitations in simulating time-delay effects. This study introduces time-shifted correlation coefficients and time-delayed transfer entropy to analyze the direction of information transmission and the time delays among environmental temperature, dam body temperature, and deformation monitoring variables. A methodology is proposed to determine the dimensions of temperature factors and their respective time delays. Utilizing a long short-term memory (LSTM) neural network integrated with Dropout regularization, a concrete dam deformation prediction model that accounts for the time delay effect of environmental temperature is developed. The results demonstrate that the proposed deformation prediction model offers superior fitting accuracy and predictive capability, effectively elucidating how environmental and dam body temperatures influence dam deformation.
混凝土坝结构对各种影响因素具有复杂的非线性特征和显著的时间滞后性。变形是一项关键的监测指标，能直接反映这些大坝的结构响应。有效的变形分析和预测模型对于准确评估混凝土坝结构的健康状况至关重要。当前的变形预测模型在模拟时延效应方面存在局限性。本研究引入时移相关系数和时延转移熵，以分析环境温度、坝体温度和变形监测变量之间的信息传递方向和时间延迟。提出了一种确定温度因子维度及其各自时间延迟的方法。利用结合了随机失活正则化的长短期记忆（LSTM）神经网络，开发了一种考虑环境温度时延效应的混凝土坝变形预测模型。结果表明，所提出的变形预测模型具有更高的拟合精度和预测能力，有效地阐明了环境温度和坝体温度如何影响大坝变形。
