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020 _a9789811041181
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024 7 _a10.1007/978-981-10-4118-1
_2doi
050 4 _aTA349-359
072 7 _aTGMD
_2bicssc
072 7 _aSCI096000
_2bisacsh
072 7 _aTGMD
_2thema
082 0 4 _a620.105
_223
100 1 _aAu, Siu-Kui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_932146
245 1 0 _aOperational Modal Analysis
_h[electronic resource] :
_bModeling, Bayesian Inference, Uncertainty Laws /
_cby Siu-Kui Au.
250 _a1st ed. 2017.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2017.
300 _aXXIII, 542 p. 158 illus., 28 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Spectral Analysis of Deterministic Process -- Structural Dynamics -- Spectral Analysis of Stationary Stochastic Process -- Stochastic Structural Dynamics -- Ambient Data Analysis and Simulation -- Bayesian Inference -- Classical Statistical Inference -- Bayesian OMA Framework -- Single Mode Problem -- Multi-Mode Problem -- Multi-Setup Problem -- Managing identification uncertainty -- Theory of Uncertainty Laws.
520 _aThis 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.
650 0 _aMechanics, Applied.
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650 0 _aGeotechnical engineering.
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650 0 _aBuildings—Design and construction.
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650 0 _aProbabilities.
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650 1 4 _aSolid Mechanics.
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650 2 4 _aGeotechnical Engineering and Applied Earth Sciences.
_931829
650 2 4 _aBuilding Construction and Design.
_932148
650 2 4 _aProbability Theory.
_917950
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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856 4 0 _uhttps://doi.org/10.1007/978-981-10-4118-1
912 _aZDB-2-ENG
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