结构损伤识别与环境因素影响的分离是结构健康监测的关键环节，在结构健康监测中非常重要。 本项目将利用符号时间序列分析计算效率高、能够及时处理结构健康监测系统采集的大容量数据、适用于非平稳信号、以及对土木工程中的噪声具有较好抑制能力的特点，从动态Shannon熵的全新角度研究结构损伤识别以及变化的环境因素影响。 重点研究以结构动态响应的Shannon熵作为判据的结构损伤识别方法；利用小波多尺度分析的优点，以小波系数符号时间序列的动态Shannon熵分析局部损伤；通过归一化和协整分离变化的环境与结构运行因素的影响等内容。 实现在结构健康监测实践中有效地分离环境因素影响、识别结构真实损伤和提高损伤预报精度的目标。该项研究不仅具有重要的理论意义和科学价值，而且对于将结构损伤识别应用于重大工程项目具有重要的实践意义。

Structural damage identification and elimination of environmental influences are key steps and play significant roles in structural health monitoring. In this project, dynamic Shannon entropy is proposed to investigate structural damage identification and to eliminate influences of changing environment and variation of structural loading conditions from the perspective of symbolic time series analysis, which has the advantages of high computation efficiency suitable for dealing with huge volume of incoming sensing data from a structural health monitoring system, good for processing non-stationary signal, and noise insensitive feature applicable to civil engineering. The investigation is mainly focused on developing a structural damage identification method based on dynamic Shannon entropy and further combining with wavelet multiple scale resolution analysis to locate and quantify structural damage, and eliminating varying environmental and working effects with normalization and co-integration method. The project is targeted to effectively identify structural damage under changing environment and improve the accuracy of damage predication. The proposed research has not only its scientific merits and theoretical significance, but is important in the practical application of structural damage identification in critical engineering projects.