Overview
- Presents a collection of data science applied to structural health monitoring applications
- Includes experimental and field examples of detection and identification approaches
- Explains how data can be used in the decision-making process of structural maintenance and usage
Part of the book series: Structural Integrity (STIN, volume 21)
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Table of contents(22 chapters)
About this book
The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.
Editors and Affiliations
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Department of Applied and Computational Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil
Alexandre Cury
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Department of Civil Engineering, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal
Diogo Ribeiro
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Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy
Filippo Ubertini
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Structural Engineering, University of California San Diego, La Jolla, USA
Michael D. Todd
Bibliographic Information
Book Title: Structural Health Monitoring Based on Data Science Techniques
Editors: Alexandre Cury, Diogo Ribeiro, Filippo Ubertini, Michael D. Todd
Series Title: Structural Integrity
DOI: https://doi.org/10.1007/978-3-030-81716-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-81715-2Published: 24 October 2021
Softcover ISBN: 978-3-030-81718-3Published: 25 October 2022
eBook ISBN: 978-3-030-81716-9Published: 23 October 2021
Series ISSN: 2522-560X
Series E-ISSN: 2522-5618
Edition Number: 1
Number of Pages: XV, 484
Number of Illustrations: 45 b/w illustrations, 268 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Machine Learning, Statistics, general