Virginia Tech
Browse
DOCUMENT
Bearing Annotation Guidelines.pdf (693.13 kB)
ARCHIVE
Bearing Condition State Classifier.zip (129.33 MB)
DOCUMENT
README_bearings_dataset.rtf (3.9 kB)
1/0
3 files

Bearing Condition State Classification Dataset

dataset
posted on 2021-10-05, 17:22 authored by Eric Bianchi, Matthew Hebdon

This is a dataset of structural bridge bearings. The bearings have been annotated using the American Association of State Highway and Transportation Officials (AASHTO) bridge inspection condition state guidelines and Bridge Inspector's Reference Manual (BIRM). The authors have included annotation guidelines and provided examples and explanation for bearings and their respective condition state assessment. There are a total of 947 images of bearings included in the dataset. The image size is 300x300. The bearing images were obtained from the COCO-Bridge-2021+ (Bianchi) dataset for structural detail detection. The data was split 10% testing, 90% training. After training with the EfficientNet B3 model (DOI: 10.7294/16628698), we were able to obtain an F1 score of 86.4%. More details of the training, the results, the dataset, and the code may be referenced in the journal article. The GitHub repository information may be found in the journal article.

If you are using the dataset in your work, please include both the journal article and the dataset citation.

Funding

National Science Foundation Grant No. IIS-1840044

History

Publisher

University Libraries, Virginia Tech

Language

  • English (US)

Location

Virginia Tech

Usage metrics

    Civil and Environmental Engineering

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC