Bearing Condition State Classification Dataset

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 annotati...

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Hauptverfasser:	Bianchi, Eric, Hebdon, Matthew
Format:	Dataset
Sprache:	eng
Schlagworte:	Bearings Bridge Inspection Damage Detection Dataset Deep learning Machine Learning Structural Inspection
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creator	Bianchi, Eric Hebdon, Matthew
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.
doi_str_mv	10.7294/16624642.v1
format	Dataset
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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. 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identifier	DOI: 10.7294/16624642.v1
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language	eng
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source	DataCite
subjects	Bearings Bridge Inspection Damage Detection Dataset Deep learning Machine Learning Structural Inspection
title	Bearing Condition State Classification Dataset
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