A risk-based fire and rescue management system
type: CreativeWork
description: This PhD focuses on developing a risk-based fire and rescue model for dwelling fires which importantly, is where most fire deaths occur each year. There are a vast number of variables to consider when modelling dwellings, for example variations will arise in terms of geographical location, fire safety arrangements, characteristics of occupants, activities of occupants, among others. As for the occurrence of fire itself, each incident will be unique in terms of time of day, type of fire, state of occupants, fire cues, etc. What all these variations signify is that the potential magnitude of the next fire event and its consequences are generally unpredictable. Because of complicated scenarios, unpredictability of outcomes, and high frequency of incidents, Fire and Rescue Serices (FRS) have to be both capable and flexible in operation; however finding the optimal way of providing emergency cover and minimizing risk is a complicated task which often results in reasoning and decisions taking place under uncertainty. In order to diminish some of this uncertainty and improve confidence in decision making, an extensive four-part Bayesian Network (BN) model is developed focusing on dwelling fires within the UK. The intention is to model the sequence of events which may occur during a fire from ignition through to extinguishment with the objective of assessing, under specified conditions, fire safety at a given location; this should assist in determining what the most important safety issues are for the purpose of improving fire prevention and mitigating consequences in order to reduce fire risk across residential communities. The model itself is broken down into four parts which can function independently or together as an integrated network. The model parts are as follows: Part I - "Initial fire development". Part II - "Occupancy response and further fire development". Part III - "Advanced fire situation and consequences". Part IV - "Fire response time module". Within the p…
abstract: This PhD focuses on developing a risk-based fire and rescue model for dwelling fires which importantly, is where most fire deaths occur each year. There are a vast number of variables to consider when modelling dwellings, for example variations will arise in terms of geographical location, fire safety arrangements, characteristics of occupants, activities of occupants, among others. As for the occurrence of fire itself, each incident will be unique in terms of time of day, type of fire, state of occupants, fire cues, etc. What all these variations signify is that the potential magnitude of the next fire event and its consequences are generally unpredictable. Because of complicated scenarios, unpredictability of outcomes, and high frequency of incidents, Fire and Rescue Serices (FRS) have to be both capable and flexible in operation; however finding the optimal way of providing emergency cover and minimizing risk is a complicated task which often results in reasoning and decisions taking place under uncertainty. In order to diminish some of this uncertainty and improve confidence in decision making, an extensive four-part Bayesian Network (BN) model is developed focusing on dwelling fires within the UK. The intention is to model the sequence of events which may occur during a fire from ignition through to extinguishment with the objective of assessing, under specified conditions, fire safety at a given location; this should assist in determining what the most important safety issues are for the purpose of improving fire prevention and mitigating consequences in order to reduce fire risk across residential communities. The model itself is broken down into four parts which can function independently or together as an integrated network. The model parts are as follows: Part I - "Initial fire development". Part II - "Occupancy response and further fire development". Part III - "Advanced fire situation and consequences". Part IV - "Fire response time module". Within the p…
author: Matellini, D B
keywords: HD61 Risk Management, TA Engineering (General). Civil engineering (General)
date published: 2012-01
date modified: 2026-07-24T06:30:19Z
provider: Liverpool Jon Moores University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Liverpool John Moores University