Uncertainty Modeling And Analysis In Civil Engineering

With the expansion of new technologies, materials, and the design of complex systems, the expectations of society upon engineers are becoming larger than ever. Engineers make critical decisions with potentially high adverse consequences. The current political, societal, and financial climate requires engineers to formally consider the factors of uncertainty (e.g., floods, earthquakes, winds, environmental risks) in their decisions at all levels. Uncertainty Modeling and Analysis in Civil Engineering provides a thorough report on the immediate state of uncertainty modeling and analytical methods for civil engineering systems, presenting a toolbox for solving problems in real-world situations. Topics include Neural networks Genetic algorithms Numerical modeling Fuzzy sets and operations Reliability and risk analysis Systems control Uncertainty in probability estimates This compendium is a considerable reference for civil engineers as well as for engineers in other disciplines, computer scientists, general scientists, and students.

Engineers and scientists often need to solve complex problems with incomplete information resources, necessitating a proper treatment of uncertainty and a reliance on expert opinions. Uncertainty Modeling and Analysis in Engineering and the Sciences prepares current and future analysts and practitioners to understand the fundamentals of knowledge a

Vital information on machine intelligence and pattern recognition is provided by this publication. In particular, the 31 papers discuss the ways in which uncertainty modelling and analysis are becoming an integral part of system definition and modelling in many fields. Contributions are sourced from an international base of researchers, scientists and engineers working on theoretical developments and diversified applications in engineering systems. The book is divided into two main parts. The first, Uncertainty Models and Measures, includes chapters on theoretical studies and developments carried out on uncertainty (including cognitive uncertainty and how it relates to information and intelligence), information, fuzzy logic, expert systems and neural networks. There are also chapters on modelling uncertainty in the reliability assessment of complex systems, linguistic connectives, the principle of maximum buoyancy, uncertain evidence, inductive learning, convex modelling, new uncertainty measures and information and uncertainty.The larger second part, Applications to Engineering Systems, contains application-oriented studies in fields related to civil, electrical, energy and general engineering systems. The papers cover studies on general uncertainty types in structural engineering, bridges, transmission structures, structural reliability, structural identification, system life cycle analysis, control, construction activities, decision analysis, signal detection, risk management, product quality, military command and control, data bases, long-term projections and predictions and assessment of insurance indices.The book conveys the excitement, advances and promises that all these fields offer to our expanding information-based technological society. It also hopes to stimulate the interest of other researchers around the world who are facing the challenge of new theoretical studies and innovative technological changes.

Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.

This volume addresses the issue of uncertainty in civil engineering from design to construction. Failures do occur in practice. Attributing them to a residual system risk or a faulty execution of the project does not properly cover the range of causes. A closer scrutiny of the adopted design, the engineering model, the data, the soil-construction-interaction and the model assumptions is required. Usually, the uncertainties in initial and boundary conditions are abundant. Current engineering practice often leaves these issues aside, despite the fact that new scientific tools have been developed in the past decades that allow a rational description of uncertainties of all kinds, from model uncertainty to data uncertainty. It is the aim of this volume to have a critical look at current engineering risk concepts in order to raise awareness of uncertainty in numerical computations, shortcomings of a strictly probabilistic safety concept, geotechnical models of failure mechanisms and their implications for construction management, execution, and the juristic question of responsibility. In addition, a number of the new procedures for modelling uncertainty are explained. The book is a result of a collaborate effort of mathematicians, engineers and construction managers who met regularly in a post graduate seminar at the University of Innsbruck during the past years.

Virtually every engineer and scientist needs to be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential. The second edition of this bestselling text introduces probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. It also features expanded discussions of the analysis of variance including single- and two-factor analyses and a thorough treatment of Monte Carlo simulation. The authors clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods. Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition, was designed as both a reference and as a textbook, and it serves each purpose well. Ultimately, readers will find its content of great value in problem solving and decision making, particularly in practical applications.

The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.