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Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 3 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E

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Vijayaraj Nagarajan,Mohamed O. Elasrit make it difficult to discern damage from environmental and operational (E&O) variability. Therefore, an improved process for identifying features that are sensitive to damage while insensitive to E&O effects is needed. In this study a SHM approach that utilizes causality metrics is proposed. The a
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Sk Mohiuddin,Samir Malakar,Ram Sarkary resources to prepare a large volume of the ground truth of a dataset labeled at the pixel level. Hybrid crack segmentation (Kang et al., Autom Constr 118:103291, 2020) is based on the integration of a faster region-based convolutional neural network (faster R-CNN) as the deep learning-based object
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https://doi.org/10.1007/978-3-030-75529-4d dynamics technology for structural health monitoring is being explored in order to optimize cost and improve performance. However, current imagers capture both the dynamic and static portions of a scene when only the dynamic portion is needed. The capture of both static and dynamic portions of a s
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Sk Mohiuddin,Samir Malakar,Ram Sarkarat are not captured by the mathematical model assumed. These models are often reduced order models (ROM) that have simplified physics or have been obtained through data-driven techniques, such as trained neural networks. In this paper, we evaluate two data assimilation techniques to perform paramete
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