Clustering and Deep Learning Techniques for Structural Health Monitoring

Abstract

This thesis proposes the development and application of clustering and deep learning techniques for improved automated modal identification, lost vibration data recovery, vibration signal denoising, and dynamic response reconstruction under operational and extreme loading conditions in the area of structural health monitoring. The effectiveness and performances of the proposed approaches are validated by numerical and experimental studies. The outstanding results demonstrate that these proposed approaches are reliable and very promising for practical applications.