Data-Driven and Machine Learning Frameworks for Condition Assessment of Plate Structure using Elastic Waves

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dc.contributor.authorKalimullah, Nur Mahammad Mussa
dc.date.accessioned2024-12-06T11:01:23Z
dc.date.available2024-12-06T11:01:23Z
dc.date.issued2024
dc.descriptionSupervisor: Shelke, Amit
dc.description.abstractThe 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.
dc.identifier.otherROLL NO.186104113
dc.identifier.urihttps://gyan.iitg.ac.in/handle/123456789/2704
dc.language.isoen
dc.relation.ispartofseriesTH-3449
dc.titleData-Driven and Machine Learning Frameworks for Condition Assessment of Plate Structure using Elastic Waves
dc.typeThesis
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