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Estimating the Statistical Distribution of Human Damage Produced by a Fire in a Building Using Bayesian Belief Nets

[+] Author Affiliations
Daniela M. Hanea, Ben J. M. Ale

Delft University of Technology

Paper No. IMECE2005-79875, pp. 49-55; 7 pages
doi:10.1115/IMECE2005-79875
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Engineering/Technology Management
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Engineering and Technology Management Group
  • ISBN: 0-7918-4230-4 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

The complexity of the cities’ layout and other public spaces, together with the large number of people involved leads to increased strain on the resources of emergency responders. An accident, such as a fire, remains a rare event so it is difficult for those in charge of preparing for an emergency and deciding on the acceptability of risk to get a picture of such an event. The interest of all emergency response agencies is to minimize the impact of disaster events on the entities of interest, which include first of all the human population. For this, there is need for a tool that helps the decision makers estimate the distribution of the fire outcome, given different information about the environment in which the fire takes place. This paper discusses the possibility of using continuous Bayesian belief nets for the study of the factors that influence the risk to which the people involved in a building fire are exposed, and how these factors influence the risk. The big advantage of Bayesian belief net approach is that it can model uncertain events. The distribution of the variables of interest can be easily updated given information about some of the other variables. Moreover, the intuitive visual representation of the problem at hand can help people to understand complex systems or processes, like a fire in a building. In this study, the approach is tested for a small example and the results are analyzed. The possibility of extending this method to a more complex model is discussed.

Copyright © 2005 by ASME

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