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Operational Modal Analysis

Modeling, Bayesian Inference, Uncertainty Laws

Authors: Au, Siu-Kui

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  • Presents operational modal analysis, employing a coherent and comprehensive Bayesian framework for modal identification
     
    Covers materials from introductory to advanced level, which are classified accordingly to ensure easy access for readers from different fields
     
    Includes stochastic modeling, theoretical formulations, computational algorithms, and practical applications

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eBook $189.00
price for USA in USD (gross)
  • ISBN 978-981-10-4118-1
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  • Immediate eBook download after purchase
Hardcover $249.99
price for USA in USD
  • ISBN 978-981-10-4117-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $249.99
price for USA in USD
  • ISBN 978-981-13-5053-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This book presents operational modal analysis (OMA), employing a coherent and comprehensive Bayesian framework for modal identification and covering stochastic modeling, theoretical formulations, computational algorithms, and practical applications. Mathematical similarities and philosophical differences between Bayesian and classical statistical approaches to system identification are discussed, allowing their mathematical tools to be shared and their results correctly interpreted. 
Many chapters can be used as lecture notes for the general topic they cover beyond the OMA context. After an introductory chapter (1), Chapters 2–7 present the general theory of stochastic modeling and analysis of ambient vibrations. Readers are first introduced to the spectral analysis of deterministic time series (2) and structural dynamics (3), which do not require the use of probability concepts. The concepts and techniques in these chapters are subsequently extended to a probabilistic context in Chapter 4 (on stochastic processes) and in Chapter 5 (on stochastic structural dynamics). In turn, Chapter 6 introduces the basics of ambient vibration instrumentation and data characteristics, while Chapter 7 discusses the analysis and simulation of OMA data, covering different types of data encountered in practice. Bayesian and classical statistical approaches to system identification are introduced in a general context in Chapters 8 and 9, respectively. 
Chapter 10 provides an overview of different Bayesian OMA formulations, followed by a general discussion of computational issues in Chapter 11. Efficient algorithms for different contexts are discussed in Chapters 12–14 (single mode, multi-mode, and multi-setup). Intended for readers with a minimal background in mathematics, Chapter 15 presents the ‘uncertainty laws’ in OMA, one of the latest advances that establish the achievable precision limit of OMA and provide a scientific basis for planning ambient vibration tests. Lastly Chapter 16 discusses the mathematical theory behind the results in Chapter 15, addressing the needs of researchers interested in learning the techniques for further development. Three appendix chapters round out the coverage.
This book is primarily intended for graduate/senior undergraduate students and researchers, although practitioners will also find the book a useful reference guide. It covers materials from introductory to advanced level, which are classified accordingly to ensure easy access. Readers with an undergraduate-level background in probability and statistics will find the book an invaluable resource, regardless of whether they are Bayesian or non-Bayesian.

About the authors

Dr. Au is Professor of Uncertainty, Reliability & Risk in the Center for Engineering Dynamics and Institute for Risk & Uncertainty, University of Liverpool (UK); and Chutian Professor in the School of Water Resources & Hydropower Engineering, Wuhan University (China). He holds a PhD (2001, Caltech) in civil engineering and has been working in the area of the monograph for over twenty years. He performs fundamental and applied research in engineering risk methods and structural health monitoring. He has developed an advanced Monte Carlo method called Subset Simulation that has found applications in many disciplines, e.g., civil, mechanical, aerospace, electrical and nuclear engineering. He is experienced in full-scale dynamic testing of structures and has consulted on vibration projects on long-span pedestrian bridges, large-span floors, super-tall buildings and micro-tremors. Dr. Au is recipient of the IASSAR Junior Research Prize (2005), Nishino Prize (2011), JSPS Fellowship (2014) and Tan Chin Tuan Fellowship (2015).

Table of contents (16 chapters)

Table of contents (16 chapters)

Buy this book

eBook $189.00
price for USA in USD (gross)
  • ISBN 978-981-10-4118-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $249.99
price for USA in USD
  • ISBN 978-981-10-4117-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $249.99
price for USA in USD
  • ISBN 978-981-13-5053-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Operational Modal Analysis
Book Subtitle
Modeling, Bayesian Inference, Uncertainty Laws
Authors
Copyright
2017
Publisher
Springer Singapore
Copyright Holder
Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-10-4118-1
DOI
10.1007/978-981-10-4118-1
Hardcover ISBN
978-981-10-4117-4
Softcover ISBN
978-981-13-5053-5
Edition Number
1
Number of Pages
XXIII, 542
Number of Illustrations
130 b/w illustrations, 28 illustrations in colour
Topics