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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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發(fā)表于 2025-3-21 17:19:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Science in Engineering, Volume 9
副標(biāo)題Proceedings of the 3
編輯Ramin Madarshahian,Francois Hemez
視頻videohttp://file.papertrans.cn/264/263129/263129.mp4
叢書名稱Conference Proceedings of the Society for Experimental Mechanics Series
圖書封面Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 3 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E
描述.Data Science and Engineering Volume 9: Proceedings of the 39th?IMAC,.?.A Conference and Exposition on Structural Dynamics, 2021,?.the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:.Data Science in Engineering Applications.Engineering Mathematics.Computational Methods in Engineering.
出版日期Conference proceedings 2022
關(guān)鍵詞data science; Modal Analysis; Structural Dynamics; Dynamic Substructures; Structural Engineering; Confere
版次1
doihttps://doi.org/10.1007/978-3-030-76004-5
isbn_softcover978-3-030-76006-9
isbn_ebook978-3-030-76004-5Series ISSN 2191-5644 Series E-ISSN 2191-5652
issn_series 2191-5644
copyrightThe Society for Experimental Mechanics, Inc. 2022
The information of publication is updating

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Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaur remaining useful life. SHM therefore enables a more efficient maintenance and operational decision-making process. Traditionally, SHM has been focussed on a single structure or system. In most maintenance strategies, detected damages or defects are repaired before they can progress further. However
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Komalpreet Kaur,Yogesh Kumar,Sukhpreet Kaurhquake. The obtained information is also used for the estimation of the financial need requests, which are crucial for the rapid initiation of the recovery acts after an earthquake. This article aims at providing a data-based framework that guides a reconnaissance surveying team by actively learning
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Laura-Nicoleta Ivanciu,Gabriel Olteanferent structures. The attempts have been focussed on homogeneous and heterogeneous populations. A more general approach to transferring knowledge between structures is by considering all plausible structures as points on a multidimensional base manifold and building a fibre bundle. The idea is quit
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https://doi.org/10.1007/978-1-4614-7245-2e detection. The advancements of both machine learning (ML) algorithms and non-destructive testing (NDT) techniques offer the correct setting to successfully tackle these challenges. In this work, non-destructive testing was performed with a scanning laser Doppler vibrometer (SLDV), in order to obta
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