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Applied Research in Uncertainty Modeling and Analysis

  • Nii O. Attoh-Okine
  • Bilal M. Ayyub

Part of the International Series in Intelligent Technologies book series (ISIT, volume 20)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Noriyasu Homma, Madan M. Gupta, Masao Sakai, Makoto Yoshizawa, Kenichi Abe
    Pages 19-30
  3. James J. Buckley, Kevin D. Reilly, Xidong Zheng
    Pages 31-60
  4. James J. Buckley, Kevin D. Reilly, Xidong Zheng
    Pages 61-90
  5. Rafael E. Herrera, Mingui Sun, Ronald E. Dahl, Neal D. Ryan, Robert J. Sclabassi
    Pages 91-113
  6. Qiang Liu, Mingui Sun, Ching-Chung Li, Robert J. Sclabassi
    Pages 115-137
  7. Robyn R. Bates, Mingui Sun, Mark L. Scheuer, Robert J. Sclabassi
    Pages 139-159
  8. Luke E. K. Achenie, G. M. Ostrovsky
    Pages 161-191
  9. Rafiqul Alam Tarefder, Musharraf Zaman
    Pages 193-213
  10. Domonkos Tikk, György Biró, Jae Dong Yang
    Pages 283-302
  11. Shinya Kikuchi
    Pages 303-319
  12. Mauro Dell’Orco, Dušan Teodorović
    Pages 321-339
  13. Giulio Erberto Cantarella, Stefano de Luca
    Pages 341-368
  14. Maria Alice P. Jacques, Daliana B. L. M. Santos, Matti Pursula, Iisakki Kosonen
    Pages 399-416
  15. Jungwon Huh, Achintya Haldar, Seung Y. Lee
    Pages 417-442
  16. Hasan Katkhuda, Achintya Haldar
    Pages 461-490
  17. Tarek N. Kudsi
    Pages 513-531
  18. Back Matter
    Pages 533-545

About this book

Introduction

Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous.  In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty.

In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on theory, the authors use real-world examples to demonstrate the strength of the chosen methodology.

Applied Research in Uncertainty Modeling and Analysis concentrates on general aspects of uncertainty, modeling, and methods, and focuses on various applications, included Biomedical Engineering, Chemical Engineering, Structural Engineering, and Transportation Engineering.

Keywords

Analysis automation fuzzy model modeling ontology simulation

Editors and affiliations

  • Nii O. Attoh-Okine
    • 1
  • Bilal M. Ayyub
    • 2
  1. 1.University of DelawareNewarkUSA
  2. 2.University of MarylandCollege ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b101807
  • Copyright Information Springer Science+Business Media, Inc. 2005
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-23535-6
  • Online ISBN 978-0-387-23550-9
  • Series Print ISSN 1382-3434
  • Buy this book on publisher's site