Grid Flow: An Object-oriented Building Evacuation Model Combining Pre-movement And Movement Behaviours For Performance-based Design

Bensilum, M.; Purser, David

doi:10.3801/iafss.fss.7-941

Cited by 50 publications

(40 citation statements)

References 3 publications

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“…This has involved monitored evacuations, with video analysis of pre-movement and travel behaviours, and fire incident investigations of occupant and management behaviours, in a range of occupancy types. The results obtained, with other published data [3,4], have been used to develop behavioural scenario categories and also to develop calculation and computer simulation methods for evacuations [14]. Some actual pre-movement and evacuation times for design scenarios are presented in references [5 and 14], while further data are being compiled.…”

Section: Design Behavioural Scenariosmentioning

confidence: 99%

ASET And RSET: Addressing Some Issues In Relation To Occupant Behaviour And Tenability

Purser¹

2003

Fire Safety Science - Proceedings of the Seventh International

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Performance-based life-safety design depends on a comparison between the time required for escape (Required Safe Escape Time -RSET) and the time to loss of tenability (Available Safe Escape Time -ASET). Both include a number of stages, involving a variety of processes and requiring a range of input data. A problem for the design engineer is that while all stages need to be addressed to obtain a realistic outcome for the analysis, some aspects are reasonably well understood and quantified, while others are often oversimplified or ignored. For the RSET time line, most emphasis is usually placed upon the travel time component, representing the physical movement of occupants into and through the escape routes. However, the time required for occupants to engage in a range of behaviours before the travel phase (pre-movement time), often represents a greater component of the total escape time. Pre-movement time distributions are dependent upon key features such as occupancy type, warnings, occupant characteristics, building complexity and fire safety management strategy. It is proposed that a practical solution for the engineer is to apply pre-movement time distributions measured from monitored evacuations, fire incidents, or derived using behavioral models, and specified in terms of a number of "design behavioural scenarios" analogous to "design fire scenarios", classified according to the key features listed.A problem with the evaluation of travel time, is that most calculation methods assume no interaction between the occupants and the fire effluent. If occupants are exposed to irritant smoke, then movement speeds are likely to be reduced. A calculation method is proposed, relating predicted travel speed to smoke and irritant concentrations.The ASET time line ends when occupant incapacitation is predicted from exposure to fire effluent. This depends upon the time-concentration curves for the main toxic fire effluents, requiring inputs on smoke and toxic product yields under different fire conditions. Existing engineering calculations use only smoke density and/or carbon monoxide, with yields often treated as constants, usually for the well-ventilated fire case. A method is proposed, whereby yield data for major toxic effluent species can be obtained over a range of fire conditions, expressed in relation to the global equivalence ratio. Results are illustrated for carbon monoxide and hydrogen cyanide.

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Section: Design Behavioural Scenariosmentioning

confidence: 99%

ASET And RSET: Addressing Some Issues In Relation To Occupant Behaviour And Tenability

Purser¹

2003

Fire Safety Science - Proceedings of the Seventh International

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“…Some research results have confirmed that pre-movement time is a variable following a probability distribution, but the specific probabilistic distribution form is still unknown. Previous studies indicate that a Weibull distribution (Maclennan et al 1999), log-normal distribution (Bensilum, Purser 2002) or normal distribution may be suitable for characterizing pre-movement time. In this study, normal distribution is employed to characterize the pre-movement time.…”

Section: Fig 3 Heat Release Rate Curves Of Time-squared Firementioning

confidence: 99%

A Method for Linking Safety Factor to the Target Probability of Failure in Fire Safety Engineering

Kong

Frantzich

et al. 2014

Journal of Civil Engineering and Management

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Abstract. Ensuring occupants' safety in building fires is one of the most important aspects for fire safety engineering. Many uncertainties are inevitably introduced when estimating the occupant safety level, due to the high complexity of fire dynamics and the human behaviour in fires. Safety factor methods are traditionally employed to deal with such uncertainties. This kind of methods is easy to apply but leaves fire safety engineers unsure of the margin by which the design has failed. A method of linking safety factor and probability of failure in fire safety engineering is proposed in this study. An event tree is constructed to analyse potential fire scenarios that arise from the failure of fire protection systems. Considering uncertainties related to fire dynamics and evacuation, the traditional deterministic safety factor is considered as a random variable. Because there is no target probability of failure accepted by the whole fire safety engineering community, the concept of expected risk to life (ERL) is integrated to determine the target probability of failure. This method employs a Monte Carlo Simulation using Latin Hypercube Sampling (LHS) to calculate the required safety factor. A practical case study is conducted using the method proposed in this study.Keywords: probability of failure, safety factor, Monte Carlo simulation, fire safety engineering, uncertainty analysis. Reference to this paper should be made as follows: Kong, D.; Lu, S.; Frantzich, H.; Lo, S. M. 2013. A method for linking safety factor to the target probability of failure in fire safety engineering, Journal of Civil Engineering and Management 19(Supplement 1): S212-S221. http://dx

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“…Early attempts to estimate evacuation times for building spaces based upon simple flow calculations or hydraulic computer models have largely given way to increasingly complex modelling simulations incorporating larger numbers of behavioral and physical movement parameters and their interactions [1][2][3][4][5]. These parameters are generally validated against experimental data, but it is not always easy to determine that they are adequately represented in the models.…”

Section: Introductionmentioning

confidence: 99%

“…In experimental evacuations it has been found that evacuation times for single enclosures are very dependent upon the interactions between the pre-travel activity time (PTAT) distributions of the occupants and the travel-related features determining physical movement to and through the exits [1,7,8]. (PTAT [also know as pre-movement time] consists of the interval between the time when a warning is given and that when the first move is made by an occupant towards an exit.…”

Section: Introductionmentioning

confidence: 99%

“…Travel time parameters include exit choice, the distance between each occupant and their chosen exit, occupant densities, unimpeded and impeded movement speeds and flow rates through the exits. However, it is considered likely that for crowded cases the most important characteristics are likely to be the PTAT of the first few occupants to move and form queues at the exits, while for sparsely occupied spaces (or very wide PTAT distributions), the PTAT of the last few occupants to move, walking speeds and travel distances to the exits are likely be the dominant factors [1,6,9,10].…”

Section: Introductionmentioning

confidence: 99%

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Dependence of modelled evacuation times on key parameters and interactions

Purser¹

2008

Fire Saf. Sci.

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Times and patterns of buildings evacuations involve interactions between many behavioral parameters reflected in the increasing complexity of computer simulations. A combination of detailed GridFlow computer evacuations simulations, calculation models and experimental evacuations have been used to determine the extent to which evacuation patterns and times are largely dependent upon a small number of key parameters and interactions. Cases investigated included a single retail enclosure and multi-enclosure, multi-storey office buildings designed following UK prescriptive guidance. It is concluded that evacuation times are very dependent upon a small number of critical factors (including pre travel activity time [PTAT] distributions, exit choice ratios, maximum flow rates, merge ratios, and densities of stationary and moving groups). Even sophisticated computer simulations can give misleading results if these factors are not adequately represented.. while simple calculation methods can provide a useful first estimate of evacuation times for designers, and a useful check on the performance of more complex simulation models.

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Grid Flow: An Object-oriented Building Evacuation Model Combining Pre-movement And Movement Behaviours For Performance-based Design

Cited by 50 publications

References 3 publications

ASET And RSET: Addressing Some Issues In Relation To Occupant Behaviour And Tenability

ASET And RSET: Addressing Some Issues In Relation To Occupant Behaviour And Tenability

A Method for Linking Safety Factor to the Target Probability of Failure in Fire Safety Engineering

Dependence of modelled evacuation times on key parameters and interactions

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