Bilal M. Ayyub – författare

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.

Sponsored by the Infrastructure Resilience Division of ASCEA large portion of the world's population, infrastructure, and wealth is concentrated in locations prone to natural disasters such as earthquakes, droughts, floods, and storms, so infrastructure resilience and sustainability as system characteristics are necessary for societal endurance and survival. Enhancing infrastructure at the element, system, network, and community levels will lead not only to massive savings and conservation of resources through efficiencies but also through risk reduction to life, property, and environment, and expeditious recovery in case of natural disasters.Hazard-Resilient Infrastructure: Analysis and Design, MOP 144, provides guidance and an underlying framework for creating consistency across hazards, systems, and sectors in the design of new infrastructure systems. The book also discusses enhancing the resilience of existing systems and relates this framework to the economics associated with system lifecycle, including organizational and socioeconomic considerations.This MOP uses probabilistic methods for risk analysis and management of infrastructure projects to address uncertainties within a planning horizon timeframe effectively. This approach includes identifying and analyzing hazards, system failures, associated probabilities and consequences including direct and indirect losses, failure and recovery profiles quantification of resilience, effects on communities, economics of resilience, and technologies for enhancing resilience for new, as well as existing infrastructure. Examples and cases studies are also included.Engineers, planners, researchers, and other community stakeholders will benefit from this manual as they make assessment, and planning and design decisions related to all types of hazards and infrastructure.

This text examines the use of analytical tools developed for studying uncertainty analysis in engineering, control systems, and the sciences. It is the work of 38 contributors who have each written chapters on developed analytical methods - fuzzy logic, neural networks, simulation, and Bayesian techniques - and have applied them to uncertainty phenomena arising out of information and knowledge problems in the fields of engineering and the sciences. The book is divided into the following parts : part I reports the theoretical studies on uncertainty types, models and measures; part II reviews the applications of uncertain theoretical tools to engineering systems; part III describes the methodologies of fuzzy-neural data analysis and forecasting; part IV presents two chapters on fuzzy-neuro systems; and part V describes the methodologies for fuzzy decision making and optimization and their computational methods. The editors also provide a concluding chapter on uncertainty and uncertainty modelling.

Experts, despite their importance and value, can be double-edged swords. They can make valuable contributions from their deep base of knowledge, but those contributions may also contain their own biases and pet theories. Therefore, selecting experts, eliciting their opinions, and aggregating their opinions must be performed and handled carefully, with full recognition of the uncertainties inherent in those opinions.Elicitation of Expert Opinions for Uncertainty and Risks illuminates those uncertainties and builds a foundation of philosophy, background, methods, and guidelines that helps its readers effectively execute the elicitation process. Based on the first-hand experiences of the author, the book is filled with illustrations, examples, case studies, and applications that demonstrate not only the methods and successes of expert opinion elicitation, but also its pitfalls and failures.Studies show that in the future, analysts, engineers, and scientists will need to solve ever more complex problems and reach decisions with limited resources. This will lead to an increased reliance on the proper treatment of uncertainty and on the use of expert opinions. Elicitation of Expert Opinions for Uncertainty and Risks will help prepare you to better understand knowledge and ignorance, to successfully elicit expert opinions, to select appropriate expressions of those opinions, and to use various methods to model and aggregate opinions.

Risk Analysis in Engineering and Economics is required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each to help readers translate the discussed techniques into real-world solutions.This Second Edition:Introduces the topic of risk financeIncorporates homeland security applications throughoutOffers additional material on predictive risk managementIncludes a wealth of new and updated end-of-chapter problemsDelivers a complementary mix of theoretical background and risk methodsBrings together engineering and economics on balanced terms to enable appropriate decision makingPresents performance segregation and aggregation within a risk frameworkContains contemporary case studies, such as protecting hurricane-prone regions and critical infrastructureProvides 320+ tables and figures, over 110 diverse examples, numerous end-of-book references, and a bibliographyUnlike the classical books on reliability and risk management, Risk Analysis in Engineering and Economics, Second Edition relates underlying concepts to everyday applications, ensuring solid understanding and use of the methods of risk analysis.

The safety and serviceability of in-service structures and infrastructure systems are often threatened by natural hazards. Owners and decision-makers are especially concerned about the resilience of an asset (structure or infrastructure), namely its ability to be in readiness for, absorb, recover from, and adapt to disruptive events. Climate change is increasingly producing nonstationary natural hazards such as wind hazards, and concurrent multiple hazards may result in especially severe performance reduction.This book focuses on hazard modeling and resilience methods and their application in engineering cases. It presents the state of the art of the theory, methodology, and applications related to infrastructure resilience and offers practical approaches to enhancing resilience for engineers, policymakers, and researchers.

Virtually every engineer and scientist must 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 goal of this popular and proven book is to introduce the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purpose of data and uncertainty analysis and modeling in support of decision-making.The primary objectives to the author’s approach include: (1) introducing probability, statistics, reliability, and risk methods to students and practicing professionals in engineering and the sciences; (2) emphasizing the practical use of these methods; and (3) establishing the limitations, advantages, and disadvantages of the methods. The book was developed with an emphasis on solving real-world technological problems that engineers and scientists are asked to solve as part of their professional responsibilities.Upon graduation, engineers and scientists must have a solid academic foundation in methods of data analysis and synthesis, as the analysis and synthesis of complex systems are common tasks that confront even entry-level professionals.The underlying theory, especially the assumptions central to the methods, is presented, but then the proper application of the theory is presented through realistic examples, often using actual data. Every attempt is made to show that methods of data analysis are not independent of each other. Instead, we show that real-world problem-solving often involves applying many of the methods presented in different chapters.Probability, Statistics, and Reliability for Engineers and Scientists, here in its fourth edition, is a very popular textbook. Ultimately, readers will find its content of great value in problem-solving and decision-making, particularly in practical applications.

This widely used textbook demonstrates the power of numerical methods in the context of solving complex engineering and scientific problems. Numerical Analysis for Engineers: Methods and Applications, Third Edition helps to prepare future engineers and assists practicing engineers in understanding the fundamentals of numerical methods, especially their applications, limitations, and potentials.Each chapter contains many computational examples, as well as a section on applications that contain additional engineering examples. Each chapter also includes a set of exercise problems.The problems are designed to meet the needs of instructors in assigning homework and to help students with practicing the fundamental concepts. Although the book was developed with emphasis on engineering and technological problems, the numerical methods can also be used to solve problems in other fields of science.The text is most appropriate for a first course in numerical methods, so students would only need basic calculus and matrix algebra.The book offers three general parts: (1) introduction to numerical methods, (2) the process and application of numerical methods, and (3) optimization of engineering data. In all chapters, a proper balance between theory and the application of the numerical methods is provided.Outstanding Features:Presents a balanced mix of theory and engineering conceptsEmphasizes theoretical background and engineering applicationsIntroduces fundamental concepts in a self-contained mannerProvides numerous examples and applications to illustrate key computational algorithmsProvides end-of-chapter exercise problemsA Solutions Manual is available for instructors.

In a technological society, 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.Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition introduces the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purposes of data and uncertainty analysis and modeling in support of decision making.The third edition of this bestselling text presents 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. This provides a measure of continuity and shows the broad use of simulation as a computational tool to inform decision making processes. This edition 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 not only 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. Like its predecessors, this book continues to serve its purpose well as both a textbook and a reference. Ultimately, readers will find the content of great value in problem solving and decision making, particularly in practical applications.

Uncertainty has been a concern to engineers, managers, and scientists for many years in engineering and sciences. Uncertainty has for a long time been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous and more tools that model uncertainty than statistics have been proposed by several engineers and scientists. The tool/ method to model uncertainty in a specific context should really be choices by considering the features of the phenomenon under consideration not independently of what is known about the system and what causes uncertainty. Applied Research in Uncertainty Modeling Analysis concentrates on general aspects of uncertainty, modeling, and methods, and consists of large numbers of examples on engineering and sciences.

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.

Risk Analysis in Engineering and Economics is required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each to help readers translate the discussed techniques into real-world solutions.This Second Edition: Introduces the topic of risk financeIncorporates homeland security applications throughoutOffers additional material on predictive risk managementIncludes a wealth of new and updated end-of-chapter problemsDelivers a complementary mix of theoretical background and risk methodsBrings together engineering and economics on balanced terms to enable appropriate decision makingPresents performance segregation and aggregation within a risk frameworkContains contemporary case studies, such as protecting hurricane-prone regions and critical infrastructureProvides 320+ tables and figures, over 110 diverse examples, numerous end-of-book references, and a bibliographyUnlike the classical books on reliability and risk management, Risk Analysis in Engineering and Economics, Second Edition relates underlying concepts to everyday applications, ensuring solid understanding and use of the methods of risk analysis.

Numerical Analysis for Engineers: Methods and Applications demonstrates the power of numerical methods in the context of solving complex engineering and scientific problems. The book helps to prepare future engineers and assists practicing engineers in understanding the fundamentals of numerical methods, especially their applications, limitations, and potentials. Each chapter contains many computational examples, as well as a section on applications that contain additional engineering examples. Each chapter also includes a set of exercise problems. The problems are designed to meet the needs of instructors in assigning homework and to help students with practicing the fundamental concepts. Although the book was developed with emphasis on engineering and technological problems, the numerical methods can also be used to solve problems in other fields of science.

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 and ignorance, how to model and analyze uncertainty, and how to select appropriate analytical tools for particular problems.This volume covers primary components of ignorance and their impact on practice and decision making. It provides an overview of the current state of uncertainty modeling and analysis, and reviews emerging theories while emphasizing practical applications in science and engineering.The book introduces fundamental concepts of classical, fuzzy, and rough sets, probability, Bayesian methods, interval analysis, fuzzy arithmetic, interval probabilities, evidence theory, open-world models, sequences, and possibility theory. The authors present these methods to meet the needs of practitioners in many fields, emphasizing the practical use, limitations, advantages, and disadvantages of the methods.

The functionality of modern structural, mechanical and electrical or electronic systems depends on their ability to perform under uncertain conditions. Consideration of uncertainties and their effect on system behavior is an essential and integral part of defining systems. In eleven chapters, leading experts present an overview of the current state of uncertainty modeling, analysis and design of large systems in four major areas: finite and boundary element methods (common structural analysis techniques), fatigue, stability analysis, and fault-tolerant systems. The content of this book is unique; it describes exciting research developments and challenges in emerging areas, and provide a sophisticated toolbox for tackling uncertainty modeling in real systems.