Ayyub B. (ed.) Applied Research in Uncertainty Modeling and Analysis

Springer, 2005. — 545 p. — (International Series in Intelligent Technologies, vol. 20). — ISBN: 978-0-387-23550-9; ISBN: 978-0-387-23535-6Uncertainty 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.Sections:
Philosophical and theoretical foundation of uncertainty
Biomedical and chemical engineering applications
Application of neural networks and artificial life to civil infrastructure systems
Management of risks and document classification problems
Applications of uncertainty theory to a variety of transportation engineering problems
Uncertainties in structural engineering