Kalimullah, Nur Mahammad Mussa2024-12-062024-12-062024ROLL NO.186104113https://gyan.iitg.ac.in/handle/123456789/2704Supervisor: Shelke, AmitThe evolving domain of structural health monitoring (SHM) is crucial for ensuring the integrity and extending the service life of engineering structures. This thesis presents a suite of data-driven and machine learning frameworks developed to enhance the condition assessment of plate structures, particularly focusing on the complexities of piezoelectric materials and anisotropic composites. In a comprehensive exploration of data-driven and machine learning frameworks for assessing the condition of plate structures, this dissertation presents a series of interconnected studies, each contributing to the advanced insights and application in SHM, particularly focusing on piezoelectric materials and anisotropic composites.enThesis