{"data":{"id":"10.17603/ds2-wneq-sg96","type":"dois","attributes":{"doi":"10.17603/ds2-wneq-sg96","prefix":"10.17603","suffix":"ds2-wneq-sg96","identifiers":[{"identifier":"PRJ-5751","identifierType":"Project ID"}],"alternateIdentifiers":[{"alternateIdentifierType":"Project ID","alternateIdentifier":"PRJ-5751"}],"creators":[{"name":"Bai, Yongsheng","givenName":"Yongsheng","familyName":"Bai","affiliation":[],"nameIdentifiers":[]},{"name":"Sezen, Halil","givenName":"Halil","familyName":"Sezen","affiliation":[],"nameIdentifiers":[]},{"name":"Yilmaz, Alper","givenName":"Alper","familyName":"Yilmaz","affiliation":[],"nameIdentifiers":[]},{"name":"Qin, Rongjun","givenName":"Rongjun","familyName":"Qin","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Detection of Cracking and Spalling Damage in Buildings and Bridges."},{"title":"in Detection of Cracking and Spalling Damage in Buildings and Bridges","titleType":"Subtitle"}],"publisher":"Designsafe-CI","container":{},"publicationYear":2024,"subjects":[{"subject":"Deep learning"},{"subject":"structural damage"},{"subject":"crack detection"},{"subject":"spalling detection"},{"subject":"structural damage detection"},{"subject":"structural health monitoring"}],"contributors":[{"name":"Texas State University (txstate.edu)","nameType":"Organizational","contributorType":"HostingInstitution","affiliation":[],"nameIdentifiers":[]},{"name":"Ohio State University","nameType":"Organizational","contributorType":"HostingInstitution","affiliation":[],"nameIdentifiers":[]},{"name":"Ohio State University","nameType":"Organizational","contributorType":"HostingInstitution","affiliation":[],"nameIdentifiers":[]}],"dates":[],"language":"en","types":{"ris":"RPRT","bibtex":"techreport","citeproc":"report","schemaOrg":"Report","resourceType":"Report","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[],"descriptions":[{"lang":"en-Us","description":"This is part of an NSF project (the information is this link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193\u0026HistoricalAwards=false}. In this project, over 2,200 images are used to label cracks and spalling on buildings and bridges damaged during extreme events. The data format is MS-COCO when the boundaries of the damage are manually drawn with polygon lines. The training and validation datasets are uploaded for the research community.  The reference GitHub link is here: https://github.com/Bai426/Damage-Detection-with-COCO-data-and-Mask-R-CNN. If you think the data is useful to your research, please cite the following publications:\n[1] Bai Y., Zha B., Sezen H., Yilmaz A. (2023). Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Structural Health Monitoring. 2023;22(1):338-352. doi:10.1177/14759217221083649\n[2] Bai Y., Sezen H., Yilmaz A. (2021). \"End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales,\" 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 6640-6647, doi: 10.1109/ICPR48806.2021.9413041.\n[3] Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 161-168.\n[4] Bai Y. (2022). Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation at Ohio State University.\n","descriptionType":"Abstract"},{"lang":"en-Us","description":"This is part of an NSF project (the information is this link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193\u0026HistoricalAwards=false}. In this project, over 2,200 images are used to label cracks and spalling on buildings and bridges damaged during extreme events. The data format is MS-COCO when the boundaries of the damage are manually drawn with polygon lines. The training and validation datasets are uploaded for the research community.  The reference GitHub link is here: https://github.com/Bai426/Damage-Detection-with-COCO-data-and-Mask-R-CNN. If you think the data is useful to your research, please cite the following publications:\n[1] Bai Y., Zha B., Sezen H., Yilmaz A. (2023). Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Structural Health Monitoring. 2023;22(1):338-352. doi:10.1177/14759217221083649\n[2] Bai Y., Sezen H., Yilmaz A. (2021). End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales, 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 6640-6647, doi: 10.1109/ICPR48806.2021.9413041.\n[3] Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 161-168.\n[4] Bai Y. (2022). Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation at Ohio State university.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"Field Testing of Concrete Buildings for Damage and Collapse Assessment","funderName":"National Science Foundation","awardNumber":"2036193"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
  <identifier identifierType="DOI">10.17603/DS2-WNEQ-SG96</identifier>
  <creators>
    <creator>
      <creatorName>Bai, Yongsheng</creatorName>
      <givenName>Yongsheng</givenName>
      <familyName>Bai</familyName>
    </creator>
    <creator>
      <creatorName>Sezen, Halil</creatorName>
      <givenName>Halil</givenName>
      <familyName>Sezen</familyName>
    </creator>
    <creator>
      <creatorName>Yilmaz, Alper</creatorName>
      <givenName>Alper</givenName>
      <familyName>Yilmaz</familyName>
    </creator>
    <creator>
      <creatorName>Qin, Rongjun</creatorName>
      <givenName>Rongjun</givenName>
      <familyName>Qin</familyName>
    </creator>
  </creators>
  <titles>
    <title>Detection of Cracking and Spalling Damage in Buildings and Bridges.</title>
    <title titleType="Subtitle">in Detection of Cracking and Spalling Damage in Buildings and Bridges</title>
  </titles>
  <publisher>Designsafe-CI</publisher>
  <publicationYear>2024</publicationYear>
  <resourceType resourceTypeGeneral="Dataset">Report</resourceType>
  <subjects>
    <subject>Deep learning</subject>
    <subject> structural damage</subject>
    <subject> crack detection</subject>
    <subject> spalling detection</subject>
    <subject>structural damage detection</subject>
    <subject> structural health monitoring</subject>
  </subjects>
  <contributors>
    <contributor contributorType="HostingInstitution">
      <contributorName nameType="Organizational">Texas State University (txstate.edu)</contributorName>
    </contributor>
    <contributor contributorType="HostingInstitution">
      <contributorName nameType="Organizational">Ohio State University</contributorName>
    </contributor>
    <contributor contributorType="HostingInstitution">
      <contributorName nameType="Organizational">Ohio State University</contributorName>
    </contributor>
  </contributors>
  <language>English</language>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="Project ID">PRJ-5751</alternateIdentifier>
  </alternateIdentifiers>
  <sizes/>
  <formats/>
  <version>1</version>
  <descriptions>
    <description xml:lang="en-Us" descriptionType="Abstract">This is part of an NSF project (the information is this link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193&amp;HistoricalAwards=false}. In this project, over 2,200 images are used to label cracks and spalling on buildings and bridges damaged during extreme events. The data format is MS-COCO when the boundaries of the damage are manually drawn with polygon lines. The training and validation datasets are uploaded for the research community.  The reference GitHub link is here: https://github.com/Bai426/Damage-Detection-with-COCO-data-and-Mask-R-CNN. If you think the data is useful to your research, please cite the following publications:
[1] Bai Y., Zha B., Sezen H., Yilmaz A. (2023). Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Structural Health Monitoring. 2023;22(1):338-352. doi:10.1177/14759217221083649
[2] Bai Y., Sezen H., Yilmaz A. (2021). "End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 6640-6647, doi: 10.1109/ICPR48806.2021.9413041.
[3] Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 161-168.
[4] Bai Y. (2022). Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation at Ohio State University.
</description>
    <description xml:lang="en-Us" descriptionType="Abstract">This is part of an NSF project (the information is this link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193&amp;HistoricalAwards=false}. In this project, over 2,200 images are used to label cracks and spalling on buildings and bridges damaged during extreme events. The data format is MS-COCO when the boundaries of the damage are manually drawn with polygon lines. The training and validation datasets are uploaded for the research community.  The reference GitHub link is here: https://github.com/Bai426/Damage-Detection-with-COCO-data-and-Mask-R-CNN. If you think the data is useful to your research, please cite the following publications:
[1] Bai Y., Zha B., Sezen H., Yilmaz A. (2023). Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Structural Health Monitoring. 2023;22(1):338-352. doi:10.1177/14759217221083649
[2] Bai Y., Sezen H., Yilmaz A. (2021). End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales, 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 6640-6647, doi: 10.1109/ICPR48806.2021.9413041.
[3] Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 161-168.
[4] Bai Y. (2022). Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation at Ohio State university.</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>National Science Foundation</funderName>
      <awardNumber>2036193</awardNumber>
      <awardTitle>Field Testing of Concrete Buildings for Damage and Collapse Assessment</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
","url":"https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-5751/#detail-8c4c5039-ea89-478b-97b0-b4ea7ce70f06","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":382,"viewsOverTime":[{"yearMonth":"2024-11","total":8},{"yearMonth":"2024-12","total":38},{"yearMonth":"2025-01","total":18},{"yearMonth":"2025-02","total":12},{"yearMonth":"2025-03","total":32},{"yearMonth":"2025-04","total":21},{"yearMonth":"2025-05","total":25},{"yearMonth":"2025-06","total":7},{"yearMonth":"2025-07","total":16},{"yearMonth":"2025-08","total":34},{"yearMonth":"2025-09","total":40},{"yearMonth":"2025-10","total":19},{"yearMonth":"2025-11","total":2},{"yearMonth":"2025-12","total":13},{"yearMonth":"2026-01","total":15},{"yearMonth":"2026-02","total":8},{"yearMonth":"2026-03","total":8},{"yearMonth":"2026-04","total":17},{"yearMonth":"2026-05","total":4},{"yearMonth":"2026-05","total":1},{"yearMonth":"2026-06","total":16},{"yearMonth":"2026-07","total":14},{"yearMonth":"2026-08","total":14}],"downloadCount":143,"downloadsOverTime":[{"yearMonth":"2024-11","total":1},{"yearMonth":"2024-12","total":3},{"yearMonth":"2025-01","total":3},{"yearMonth":"2025-02","total":0},{"yearMonth":"2025-03","total":10},{"yearMonth":"2025-04","total":5},{"yearMonth":"2025-05","total":4},{"yearMonth":"2025-06","total":3},{"yearMonth":"2025-07","total":6},{"yearMonth":"2025-08","total":12},{"yearMonth":"2025-09","total":17},{"yearMonth":"2025-10","total":9},{"yearMonth":"2025-11","total":1},{"yearMonth":"2025-12","total":6},{"yearMonth":"2026-01","total":12},{"yearMonth":"2026-02","total":4},{"yearMonth":"2026-03","total":4},{"yearMonth":"2026-04","total":12},{"yearMonth":"2026-05","total":2},{"yearMonth":"2026-06","total":11},{"yearMonth":"2026-07","total":11},{"yearMonth":"2026-08","total":7}],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-11-27T01:32:57.000Z","registered":"2024-11-27T01:32:58.000Z","published":"2024","updated":"2025-06-02T15:59:07.000Z"},"relationships":{"client":{"data":{"id":"tdl.tacc","type":"clients"}},"provider":{"data":{"id":"hakl","type":"providers"}},"media":{"data":{"id":"10.17603/ds2-wneq-sg96","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}