3DIFICE: A Synthetic Dataset for Training Computer Vision Algorithms to Recognize Earthquake Damage to Reinforced Concrete Structures
Cite this dataset:
Levine, Nathaniel (2022): 3DIFICE: A Synthetic Dataset for Training Computer Vision Algorithms to Recognize Earthquake Damage to Reinforced Concrete Structures. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6415287_V1
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Dataset Description

3DIFICE: 3-dimensional Damage Imposed on Frame structures for Investigating Computer vision-based Evaluation methods

This dataset contains 1,396 synthetic images and label maps with various types of earthquake damage imposed on reinforced concrete frame structures. Damage includes: cracking, spalling, exposed transverse rebar, and exposed longitudinal rebar. Each image has an associated label map that can be used for training machine learning algorithms to recognize the various types of damage.

Subject Technology and Engineering
Keywords computer vision; earthquake engineering; structural health monitoring; civil engineering; structural engineering;
License CC BY
Corresponding Creator Nathaniel Levine
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Version DOI Comment Publication Date
1 10.13012/B2IDB-6415287_V1 2022-09-29
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update: {"nested_updated_at"=>[nil, Thu, 03 Nov 2022 23:55:06.589378000 UTC +00:00]} 2024-01-03T18:24:06Z
update: {"keywords"=>["computer vision; earthquake engineering; structural health monitoring; ", "computer vision; earthquake engineering; structural health monitoring; civil engineering; structural engineering; "]} 2022-09-30T13:35:45Z