Title: A Computer Vision-Based Structural Health Monitoring Framework: Feature-Mining of Damage for Predictive Numerical Simulations
Abstract: Most of the critical defects in structural components can be invisible on the surface, mainly throughout the early stages of deterioration, causing their timely detection to be a challenge. Assessing the actual and accurate 3D form and extent of interior defects is a complicated and also cumbersome task, unexpectedly with the developments in non-destructive testing techniques. Unlike the majority of traditional methods based on specialized forms of surface-penetrating waves or radiation imaging, this research uses optical cameras for full-field sensing of surface strains and deformations using the 3D Digital Image Correlation (3D DIC) technique as the basis for damage identification. This data-rich representation of the behavior of the structural component is then leveraged in an inverse mechanical problem to reconstruct the underlying subsurface abnormalities. The inverse problem is solved through a topology optimization formulation that iteratively adjusts a fine-tuned finite element model of the structure to infer abnormalities within the structure.
Date: March 27th from 02:30-4:30 pm
Location: Zoom meeting room (details below)
Chair: Professor Jose Gomez
Advisor: Professor Devin Harris
Professor Osman Ozbulut
Professor Nada Basit
Professor Arsalan Heydarian
Mehrdad Shafiei Dizaji is inviting you to a scheduled Zoom meeting.
Topic: Mehrdad S. Dizaji's Ph.D. defense
Time: Mar 27, 2020, 02:30 PM Eastern Time (the US and Canada)
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Meeting ID: 251 020 794
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