Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning

Digital Twins technology is revolutionizing decision-making in scientific research by integrating models and simulations with real-time data. Unlike traditional Structural Health Monitoring methods, which rely on computationally intensive Digital Image Correlation and have limitations in real-time d...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Author: Mehrdad Shafiei Dizaji
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Correlation
Data integration
Deep learning
Deformation analysis
Digital imaging
Digital twins
Machine learning
Measurement techniques
Neural networks
Pattern analysis
Real time
Speckle patterns
Structural health monitoring
Three dimensional analysis
Time measurement
Two dimensional analysis
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Summary:Digital Twins technology is revolutionizing decision-making in scientific research by integrating models and simulations with real-time data. Unlike traditional Structural Health Monitoring methods, which rely on computationally intensive Digital Image Correlation and have limitations in real-time data integration, this research proposes a novel approach using Artificial Intelligence. Specifically, Convolutional Neural Networks are employed to analyze structural behaviors in real-time by correlating Digital Image Correlation speckle pattern images with deformation fields. Initially focusing on two-dimensional speckle patterns, the research extends to three-dimensional applications using stereo-paired images for comprehensive deformation analysis. This method overcomes computational challenges by utilizing a mix of synthetically generated and authentic speckle pattern images for training the Convolutional Neural Networks. The models are designed to be robust and versatile, offering a promising alternative to traditional measurement techniques and paving the way for advanced applications in three-dimensional modeling. This advancement signifies a shift towards more efficient and dynamic structural health monitoring by leveraging the power of Artificial Intelligence for real-time simulation and analysis.
ISSN:2331-8422