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Creators/Authors contains: "Cetiner, B.M."

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  1. ; ; ; ; (, 14th Americas Conference on Wind Engineering)
    This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, Proceedings of the 14th Americas Conference on Wind Engineering)
    Accepted Manuscript Full Text Available