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Found 4 papers, 0 papers with code
Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events
In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes.
Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method
This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments.
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse
In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes.
