Title of Dataset: "Bearing Condition State Classification Dataset"
Author(s): Eric Bianchi - Virginia Tech, Department of Civil and Environmental Engineering
Matt Hebdon - UT Austin, Department of Civil, Architectural & Environmental Engineering 
Corresponding Author Email Address: Eric Bianchi - beric7@vt.edu
Categories: GROUP 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING; 
GROUP 0905 CIVIL ENGINEERING.
Group: Department of Civil and Environmental Engineering - Structural Engineering and Materials 
Item Type: Dataset
Keywords: Image Classification; Machine Learning, Deep Learning; Structural Inspection; Bearings; Bridge Inspection; Dataset
Description: 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
Resource Title: "Development of Open-source Collaborative Structural Inspection Datasets"
Other References: For the codebase https://github.com/beric7/structural_inspection_main: For trained models https://doi.org/10.7294/16628698.
License: CC-0
Publisher: University Libraries, Virginia Tech
Language: English
Location: Not applicable
Files/Folders in Dataset and Description of Files:
Bearing Condition State Classifier - Main folder
	original - original image sizes
		Test - test images
			1 - good condition state rating
			2 - fair condition state rating
			3 - poor condition state rating
			4 - severe condition state rating
		Train - training images
			1 - good condition state rating
			2 - fair condition state rating
			3 - poor condition state rating
			4 - severe condition state rating
	300x300 - resized images to 300x300 pixels
		Test - test images (1-4 condition states)
		Train - training images (1-4 condition states)
	Bearing Annotation Guidelines.pdf - Guidelines for labeling bearings for classification