非接触式全场振动测量技术具有低监测环境依赖、高识别精度特点，探索其在海洋环境下结构损伤识别应用对于建立复杂环境下健康监测系统具有重要意义。现有海洋结构桩基损伤识别通常基于振动传感器信号，以结构模态及衍生特征为依据进行损伤判别，缺乏对于海洋结构桩基连续全场位移响应整体特征提取与损伤识别研究。基于深度学习无监督自编码方法，以环境激励下连续高速三维数字图像相关（three-dimensional digital image correlation, 3D-DIC）位移场为输入，自适应提取位移场空间维度特征，保持时间维度的连续性，构建位移场异变感应指标和结构损伤识别指标，实现海洋结构局部损伤识别。进一步通过高桩码头实验模型验证了所提方法的适用性和有效性。分析和实验结果表明，本文提出的深度学习无监督自编码方法能够有效实现桩基损伤识别，为连续全场位移响应的自适应损伤识别提供了新策略。

Since the non-contact full-field vibration measurement technique is characterized by low cost and high accuracy, exploring its application for offshore structural damage identification is of great significance for establishing a health monitoring system. Existing offshore structure pile damage identification is usually based on vibration sensor signals, structural modal and derived features for damage identification, and there is a lack of research on overall feature extraction and damage identification for continuous full-field displacement response of pile foundations for offshore structures. Based on the deep learning unsupervised autoencoder method, the continuous 3D-DIC(three-dimensional digital image correlation) displacement field under environmental excitation was used as the input, the spatial dimension features of the displacement field were adaptively extracted, the continuity of the temporal dimension was maintained, and the displacement anomaly index and damage detection index were constructed, so as to realize the identification of local damages to the offshore structure. The applicability and effectiveness of the proposed method are also verified by an experimental model of a high pile wharf. The analytical and experimental results show that the deep learning unsupervised autoencoder method proposed can effectively realize the damage identification of pile foundation, which provides a new strategy for the adaptive damage identification of continuous full-field displacement response.