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This title offers an overview of the fundamentals and practice applications of probability and statistics, microeconomics, engineering economics, hard and soft systems analysis, and sustainable development and sustainability applications in engineering planning.
This book integrates the basic theories (GST and Parson’s AGIL framework), applying them to the components of social systems, state-run and business firms. China’s development experience offers a valuable case study that can provide readers deeper insights into this comparatively young discipline, and into China. Though the discipline of systems engineering and its application to hardware engineering system are well established, social systems engineering is an emerging discipline still being explored. This book may be the first English-language publication on this promising subject.
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 finance Incorporates homeland security applications throughout Offers additional material on predictive risk management Includes a wealth of new and updated end-of-chapter problems Delivers a complementary mix of theoretical background and risk methods Brings together engineering and economics on balanced terms to enable appropriate decision making Presents performance segregation and aggregation within a risk framework Contains contemporary case studies, such as protecting hurricane-prone regions and critical infrastructure Provides 320+ tables and figures, over 110 diverse examples, numerous end-of-book references, and a bibliography Unlike 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.
This book presents an integrated systems approach to the evaluation, analysis, design, and maintenance of civil engineering systems. Addressing recent concerns about the world's aging civil infrastructure and its environmental impact, the author makes the case for why any civil infrastructure should be seen as part of a larger whole. He walks readers through all phases of a civil project, from feasibility assessment to construction to operations, explaining how to evaluate tasks and challenges at each phase using a holistic approach. Unique coverage of ethics, legal issues, and management is also included.
With the growing emphasis on enhancing the sustainability and efficiency of industrial plants, process integration and intensification are gaining additional interest throughout the chemical engineering community. Some of the hallmarks of process integration and intensification include a holistic perspective in design, and the enhancement of material and energy intensity. The techniques are applicable for individual unit operations, multiple units, a whole industrial facility, or even a cluster of industrial plants. This book aims to cover recent advances in the development and application of process integration and intensification. Specific applications are reported for hydraulic fracturing, palm oil milling processes, desalination, reactive distillation, reaction network, adsorption processes, herbal medicine extraction, as well as process control.
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.
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 p
Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications Use of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality.