Identifying and Assessing Damage in Infrastructure Using Topological Data Analysis and Machine Learning

Explore an innovative approach to identifying and assessing damage in infrastructure using topological data analysis and machine learning in this 43-minute conference talk by Stéphane Béreux. Discover a low-cost, imaging-based alternative to expensive laboratory diagnostics for assessing concrete damage. Learn about the application of a lightweight convolutional neural network (CNN) for crack recognition and the extraction of topological descriptors correlating with damage. Understand how topological data analysis is uniquely applied in the post-processing step. Examine the method's demonstration on real data samples of concrete affected by alkali-aggregate reaction. Gain insights into the potential of this proof-of-concept to become a fully functional and easy-to-use concrete damage assessment tool, pending more annotated data. Follow the presentation's journey from discussing the Genoa bridge disaster to exploring the chosen approach, CNN architecture, training data for segmentation, persistent histogram segmentation, crack segmentation process, and the application of concepts like Betti numbers and persistent homology in infrastructure damage assessment.