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1945 results in Engineering mathematics and programming

Page 26 of 98

  • 2 - Infrastructure Systems

  • from PART II - INFRASTRUCTURE SYSTEMS
  • Nii O. Attoh-Okine, University of Delaware
  • Book:
    Resilience Engineering
    Published online:
    05 March 2016
    Print publication:
    04 April 2016, pp 31-42

  • Summary

    Critical Infrastructure

    Infrastructure is defined as physical assets that are capable of an intended service delivery and comprised of rigid assets such as buildings, bridges, and roads and flexible assets such as utilities and facilities related to water, sewage, and power. Critical infrastructure spans across a number of key sectors including energy, finance, and information technology. An infrastructure system is an integrated structured network of interdependent entities that aid in the service delivery capability of rigid and flexible assets.

    Infrastructure evolved with society and technology and is a major key in boosting both the economy and living standards of a population. Critical infrastructure systems are therefore built to provide services and, in some cases, jobs for several generations (National Research Council 2009). Critical infrastructure is almost always subject to political and social pressures. The economic pressures in most cases result in delayed maintenance and rehabilitation. Recently, in the face of terrorists and other saboteurs, infrastructures that symbolize a nation's pride—for example, the Statue of Liberty—are also considered as critical infrastructure (Chai et al. 2011).

    The Royal Academy of Engineering (2011) extensively discussed the meanings and principles of “smart structure.” It defined smart structure as a system that uses a feed loop of data, providing evidence about the state of the infrastructure for effective maintenance and rehabilitation decision making. Therefore, the system can monitor, measure, analyze, communicate, and react based on information and data captured by multiple sensors. The principles of the smart structure include (1) large data acquisition and collection, (2) complex mathematical modeling for decision making and engineering assessment, (3) feedback systems that control collected information and improve infrastructure system operations, and (4) adaptability. The system is versatile enough to incorporate new communications and other technologies.

    There are five criteria used by different nations and jurisdictions to determine critical infrastructures: (1) health, (2) safety, (3) economy, (4) continuous functioning of government, and (5) national/society morals. The following definition of critical infrastructure was presented by the National Research Council (NRC 2002): critical infrastructure systems are defined as the water, wastewater, power, transportation, and telecommunication systems without which buildings, emergency response systems, and other infrastructure cannot operate as intended.

  • 10 - Epilogue

  • from PART II - INFRASTRUCTURE SYSTEMS
  • Nii O. Attoh-Okine, University of Delaware
  • Book:
    Resilience Engineering
    Published online:
    05 March 2016
    Print publication:
    04 April 2016, pp 150-152

  • Summary

    General

    Resilience engineering is now a new paradigm that cannot be overlooked in critical infrastructure modeling, control, and decision making. The initial concept was based on the edited work by Hollnagel et al. (2006), which was more focused on human errors, machine failures, and safety critical systems involving humans. Recently, resilience engineering has been referred to as the art of managing the unexpected or how teams or organizations become prepared to cope with surprises. These surprising events can sometimes push the system beyond its operational boundaries (Woods 2006). Therefore, the purpose of resilience engineering is to anticipate the changing potential for failure considering that plans and procedures have limits, gaps, and unforeseen errors and that the environment is very dynamic (Hollnagel et al. 2006).

    Networked and lifeline infrastructure appear to be one of the great challenges, especially in the presence of a surprise event. Designing resilient systems can limit and reduce the probability of failure and its consequences. Currently, there are a few metrics for evaluating resilience of standalone infrastructures and their interdependencies, but they are very limited and inconsistent. The most successful metrics are when only two infrastructure systems are interacting. Furthermore, there is no standard or a universal method of developing and analyzing the resilience indices of more than two interacting infrastructures or systems of interdependent networks. Buldyrev et al. (2010) and Leicht and D'Souza (2009) are a few examples of researchers leading the development of consistent and objective methods for analyzing interdependent networks. Resilience engineering is becoming a new paradigm for complex systems performance and maintenance decision making, and the resilience engineering principle fits within the sustainability framework.

    Looking Back

    Proper formulating and analyzing of resilience engineering problems and applications requires a strong background in graph theory, statistics, and machine learning algorithms. Also resilience indices are time-dependent metrics. The majority of the ideas presented in the book are toward infrastructure systems and general networks. It would be incorrect to use one metric to determine the resilience index of the systems, since for identical systems, a slightly different resilience index under different time conditions; therefore, the resilience index should be used as a guide. For example, in more sophisticated methods developed by physicists, there are many assumptions in the question used that will ultimately affect the final results.

Resilience Engineering
  • Models and Analysis
  • Nii O. Attoh-Okine
  • Published online:
    05 March 2016
    Print publication:
    04 April 2016
  • With an eye towards environmental sustainability, this book presents a step-by-step approach to formulating the resilience of civil infrastructure and energy systems. It provides a concise explanation of resilience terminology, a general overview of theoretical models and analyses, and a clear guide to practical applications, covering critical topics such as interdependent infrastructures and geologic carbon sequestration. Additionally, it contains a general introduction to selected data science topics, so readers can acquire the tools to formulate and analyze resilience engineering problems in depth. Informed by the author's extensive practical experience and thorough academic background, this book includes examples to illustrate key computational algorithms, end-of-chapter exercises and references, and an entire chapter devoted to case studies. Intended for practitioners of resilience engineering with a background in probability, this book offers a balanced blend of background, theory, and practical examples to address one of the most important emerging topics of modern times.

  • Preface

  • Jaan Kiusalaas, Pennsylvania State University
  • Book:
    Numerical Methods in Engineering with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    20 October 2015, pp ix-x

  • Summary

    This book is targeted primarily toward engineers and engineering students of advanced standing (sophomores, seniors, and graduate students). Familiarity with a computer language is required; knowledge of engineering mechanics (statics, dynamics, and mechanics of materials) is useful but not essential.

    The text places emphasis on numerical methods, not programming. Most engineers are not programmers but rather problem solvers. They want to know what methods can be applied to a given problem, their strengths and pitfalls, and how to implement them. Engineers are not expected to write computer code for basic tasks from scratch; they are more likely to utilize functions and subroutines that have already been written and tested. Thus programming by engineers is largely confined to assembling existing bits of code into a coherent package that solves the problem at hand.

    The “bit” of code is usually a function that implements a specific task. For the user, the details of the code are of secondary importance. What matters is the interface (what goes in and what comes out) and an understanding of the method on which the algorithm is based. Because no numerical algorithm is infallible, the importance of understanding the underlying method cannot be overemphasized; it is, in fact, the rationale behind learning numerical methods.

    This book attempts to conform to these views. Each numerical method is explained in detail, and its shortcomings are pointed out. The examples that follow individual topics fall into two categories: hand computations thatillustrate the inner workings of the method and small programs that show how the computer code is utilized in solving a problem. Problems that require programming are marked with ▄.

    The material consists of the usual topics covered in an engineering course on numerical methods: solution of equations, interpolation and data fitting, numerical differentiation and integration, solution of ordinary differential equations, and eigenvalue problems. The choice of methods within each topic is tilted toward relevance to engineering problems. For example, there is an extensive discussion of symmetric, sparsely populated coefficient matrices in the solution of simultaneous equations. In the same vein, the solution of eigenvalue problems concentrates on methods that efficiently extract specific eigenvalues from banded matrices.

    An important criterion used in the selection of methods was clarity. Algorithms requiring overly complex bookkeeping were rejected regardless of their efficiency and robustness.

  • 1 - Introduction to MATLAB

  • Jaan Kiusalaas, Pennsylvania State University
  • Book:
    Numerical Methods in Engineering with MATLAB®
    Published online:
    28 May 2018
    Print publication:
    20 October 2015, pp 1-26

  • Summary

    Quick Overview

    This chapter is not intended to be a comprehensive manual of MATLAB®. Our sole aim is to provide sufficient information to give you a good start. If you are familiar with another computer language, and we assume that you are, it is not difficult to pick up the rest as you go.

    MATLAB is a high-level computer language for scientific computing and data visualization built around an interactive programming environment. It is becoming the premiere platform for scientific computing at educational institutions and research establishments. The great advantage of an interactive system is that programs can be tested and debugged quickly, allowing the user to concentrate more on the principles behind the program and less on programming itself. Because there is no need to compile, link, and execute after each correction, MATLAB programs can be developed in a much shorter time than equivalent FORTRAN or C programs. On the negative side, MATLAB does not produce stand-alone applications – the programs can be run only on computers that have MATLAB installed.

    MATLAB has other advantages over mainstream languages that contribute to rapid program development:

  • • MATLAB contains a large number of functions that access proven numerical libraries, such as LINPACK and EISPACK. This means that many common tasks (e.g., solution of simultaneous equations) can be accomplished with a single function call.

  • • Extensive graphics support allows the results of computations to be plotted with a few statements.

  • • All numerical objects are treated as double-precision arrays. Thus there is no need to declare data types and carry out type conversions.

  • • MATLAB programs are clean and easy to read; they lack the syntactic clutter of some mainstream languages (e.g., C).

    • The syntax of MATLAB resembles that of FORTRAN. To get an idea of the similarities between these programming languages, let us compare the codes written in the two languages for solution of simultaneous equations Ax = b by Gauss elimination (do not worry about understanding the inner workings of the programs).

Page 26 of 98