With the rapid development of information and communication techniques, dam structural health assessment based on data collected from structural health monitoring systems has become a trend. This allows for applying data-driven methods for dam safety analysis. However, data-driven models in most related literature are statistical and shallow machine learning models, which cannot capture the time series patterns or learn from long-term dependencies of dam structural response time series. Furthermore, the effectiveness and applicability of these models are only validated in a small data set and part of monitoring points in a dam structural health monitoring system. To address the problems, this article proposes a new modeling paradigm based on various deep learning and transfer learning techniques. The paradigm utilizes one-dimensional convolutional neural networks to extract the inherent features from dam structural response–related environmental quantity monitoring data. Then bidirectional gated recurrent unit with a self-attention mechanism is used to learn from long-term dependencies, and transfer learning is utilized to transfer knowledge learned from the typical monitoring point to the others. The proposed paradigm integrates the powerful modeling capability of deep learning networks and the flexible transferability of transfer learning. Rather than traditional models that rely on experience for feature selection, the proposed deep learning–based paradigm directly utilizes environmental monitoring time series as inputs to accurately estimate dam structural response changes. A high arch dam in long-term service is selected as the case study, and three monitoring items, including dam displacement, crack opening displacement, and seepage are used as the research objects. The experimental results show that the proposed paradigm outperforms conventional and shallow machine learning–based methods in all 41 tested monitoring points, which indicates that the proposed paradigm is capable of dealing with dam structural response estimation with high accuracy and robustness.

随着信息和通信技术的迅速发展，基于从结构健康监测系统收集的数据的大坝结构健康评估已成为一种趋势。这允许将数据驱动的方法应用于大坝安全性分析。但是，大多数相关文献中的数据驱动模型是统计模型和浅层机器学习模型，它们无法捕获时间序列模式，也无法从大坝结构响应时间序列的长期依赖性中学习。此外，这些模型的有效性和适用性仅在小数据集和大坝结构健康监测系统的部分监测点中得到验证。为了解决这些问题，本文提出了一种基于各种深度学习和迁移学习技术的新建模范例。该范例利用一维卷积神经网络从大坝结构响应相关的环境量监测数据中提取固有特征。然后使用具有自注意机制的双向门控循环单元从长期依赖关系中学习，并使用转移学习将从典型监视点获得的知识转移到其他监视点。所提出的范例集成了深度学习网络的强大建模能力和迁移学习的灵活可传递性。提议的基于深度学习的范式不是依赖于经验来进行特征选择的传统模型，而是直接利用环境监测时间序列作为输入，以准确估算大坝的结构响应变化。以长期使用的高拱坝为案例研究，以大坝位移，裂隙位移和渗流三个监测项目为研究对象。实验结果表明，所提出的范例在所有41个测试监测点上均优于基于常规方法和基于浅层机器学习的方法，这表明所提出的范例能够处理大坝结构响应估计，具有较高的准确性和鲁棒性。