Modelling dwelling fire development and occupancy escape using Bayesian network

Matellini, Dante Benjamin; Wall, Alan; Jenkinson, Ian; Jin, Wang; Pritchard, Robert W.

doi:10.1016/j.ress.2013.01.001

Cited by 51 publications

(37 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parts I and II are also introduced, but for a full explanation, Matellini et al. (, ) should be consulted.…”

Section: Methodsmentioning

confidence: 99%

“…This should assist in identifying what the most pressing fire safety issues are, with a view to improve mitigation of fire consequences. This article builds on work conducted by Matellini, Wall, Jenkinson, Wang, and Pritchard (, ), in which parts one and two of the model are presented. Part one of the model covers the initial fire development in a dwelling and focuses on factors that affect human reaction to a fire, such as type of fire, time of day, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Readers wishing to gain a greater insight into parts one and two of the model should refer to Matellini et al. (, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

et al. 2018

Self Cite

View full text Add to dashboard Cite

In the United Kingdom, dwelling fires are responsible for the majority of all fire-related fatalities. The development of these incidents involves the interaction of a multitude of variables that combine in many different ways. Consequently, assessment of dwelling fire risk can be complex, which often results in ambiguity during fire safety planning and decision making. In this article, a three-part Bayesian network model is proposed to study dwelling fires from ignition through to extinguishment in order to improve confidence in dwelling fire safety assessment. The model incorporates both hard and soft data, delivering posterior probabilities for selected outcomes. Case studies demonstrate how the model functions and provide evidence of its use for planning and accident investigation.

show abstract

“…Parts I and II are also introduced, but for a full explanation, Matellini et al. (, ) should be consulted.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…For risk analysis of the impact of natural disasters on large-scale, critical infrastructure systems, electric power distribution systems, transportation systems, etc., Guikema (2009) proposed a statistical learning methodology that incorporated a diverse set of methods designed for performing inference analysis on large, complex data sets,. Matellini et al (2013) developed a Bayesian network model to investigate the evolution of fires within dwellings and to assess the probabilities of the associated consequences. Holicky and Schleich (2000) studied fire protection systems and used a Bayesian network to model a building fire from its onset to the structural collapse of the building.…”

Section: Disaster Dynamic Predictionmentioning

confidence: 99%

Emergency logistics for wildfire suppression based on forecasted disaster evolution

2017

View full text Add to dashboard Cite

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces).

show abstract

“…A fire development may produce an unforeseen scenario, which is not included under the existing plans and procedures, developed by a ship operator. Each fire incident may be unique in terms of the type of fire, time of the day, the state of occupants and fire cues (Matellini, Wall, Jenkinson, Wang & Pritchard, ). For instance, studies on the existence of fire whirls in the engine room of a ship showed that the temperature characteristic of fire whirls will influence the path shift of fire which is nearly circular (Shichuan, Liang, Yuhong & Xiang, ).…”

Section: Introductionmentioning

confidence: 99%

Effect of Human Behaviour in Shipboard Firefighting Decisions: The Case of Fire in Engine Rooms

Karahalios¹

2017

Contingencies & Crisis Mgmt

View full text Add to dashboard Cite

This study highlights the hazards associated with the development of a fire onboard a ship, with respect to the decision‐making alternatives of its captain. In this context, a risk assessment methodology is proposed, using the AHP method, to identify the expected hazards, caused by a fire in the engine room of a ship, as a part of a contingency planning. An analysis of 77 casualties, revealed that ships aged over 15 years and/or designed to carry RO‐RO cargoes, containers, or passengers, have a relatively high frequency of fire events in their engine rooms. Furthermore, the element of time was found to be a factor, which affects the human behaviour of the captain of a ship, as fire growths.

show abstract

Modelling dwelling fire development and occupancy escape using Bayesian network

Cited by 51 publications

References 24 publications

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

Emergency logistics for wildfire suppression based on forecasted disaster evolution

Effect of Human Behaviour in Shipboard Firefighting Decisions: The Case of Fire in Engine Rooms

Contact Info

Product

Resources

About