probabilistic risk analysis foundations and methods pdf

Probabilistic risk analysis foundations and methods pdf

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Probabilistic risk analysis: foundations and methods

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Mathematical Problems in Engineering

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Bedford , R. Probabilistic risk analysis: foundations and methods. N2 - Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible.

Probabilistic risk analysis: foundations and methods

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below! Probabilistic risk analysis : foundations and methods Home Probabilistic risk analysis : foundations and methods. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published Sixth printing Printed in the United Kingdom at the University Press, Cambridge A catalogue record for this publication is available from the British Library Library of Congress Cataloguing in Publication data Bedford T. Includes bibliographical references and index. ISBN 0 2 1.

Collapse risk analysis is of great significance for ensuring construction safety in foundation pits. This study proposes a comprehensive methodology for dynamic risk analysis of foundation pit collapse during construction based on a fuzzy Bayesian network FBN and a fuzzy analytical hierarchy process FAHP. Firstly, the potential risk factors contributing to foundation pit collapse are identified based on the results of statistical analysis of foundation pit collapse cases, expert inquiry, and fault tree analysis. Then, a FAHP and improved expert elicitation considering a confidence index are adopted to elicit the probability parameters of the BN. On this basis, quantitative risk reasoning and sensitivity analysis of foundation pit collapse are achieved by means of fuzzy Bayesian inference. Finally, an actual deep foundation pit in a metro station was used to illustrate a specific application of this approach, and the results were in accordance with the field observations and numerical simulation results. The proposed approach can provide effective decision-making support for planners and engineers, which is vital to the prevention and control of the occurrence of the foundation pit collapse accidents.

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Risk is defined as the probability of harmful consequences, or expected losses deaths, injuries, property, livelihoods, economic activity disrupted or environment damaged resulting from interactions between natural or human-induced hazards and vulnerable conditions UN-ISDR, , EC, Risk can presented conceptually with the following basic equation indicated in Figure 1. Figure 1: Schematic representation of risk as the multiplication of hazard, vulnerability and quantification of the exposed elements-at-risk. The various aspects of hazards, vulnerability and elements-at-risk and their interactions are also indicated. This framework focuses on the analysis of physical losses, using physical vulnerability data. Risk assessment is a process to determine the probability of losses by analyzing potential hazards and evaluating existing conditions of vulnerability that could pose a threat or harm to property, people, livelihoods and the environment on which they depend UN-ISDR, ISO defines risk assessment as a process made up of three processes: risk identification, risk analysis, and risk evaluation.

Mathematical Problems in Engineering

Uncertainty and Risk pp Cite as. The methods of engineering probabilistic risk analysis and expected-utility decision analysis share a common core: a probabilistic model of occurrences of uncertain events. This model is based on systems analysis and on the identification of an exhaustive and mutually exclusive set of scenarios, their probabilities and their consequences. The major differences are rooted in the nature and the framing of the problems that they address.

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Mathematical Problems in Engineering

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