Data-Driven and Machine Learning Frameworks for Condition Assessment of Plate Structure using Elastic Waves
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2024
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Abstract
The 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.
Description
Supervisor: Shelke, Amit