Probabilistic Framework for Assessing Risks to Structures Stemming From Accidental Explosions

Vaidogas, Egidijus Rytas

doi:10.3846/13923730.2002.10531261

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…It can be extended to assessment of damage due to the AE. This will lead to representing results of damage assessment in the form of risk [30]. The risk can be evaluated by transforming probabilities of damage events into frequencies of these events (eg, [3] for the general definition of risk used in QRA).…”

Section: The Need Of Uncertainty Modellingmentioning

confidence: 99%

Explosive Damage to Industrial Buildings: Assessment by Resampling Limited Experimental Data on Blast Loading

Vaidogas

2005

Journal of Civil Engineering and Management

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Assessment of damage to industrial buildings due to accidental explosions in air is considered. It is suggested to formulate the result of the damage assessment in the form of risk. The expression of risk embraces probabilities of foreseeable damage events (damage probabilities) and outcomes (consequences) of suffering these events. The situation is examined when blast loading imposed by an accidental explosion is predicted by a series of experiments. They yield a small‐size sample of blast loading characteristics. It is suggested to idealise the formation of explosive damage to industrial buildings by means of event trees diagrams. A quantitative analysis of these diagrams can be carried out by developing fragility functions for their branching points. Each branching point is used to represent a structural failure contributing to the final explosive damage. The fragility functions are applied to expressing the conditional probabilities of explosive damage. With these probabilities, a technique of frequentist (Fisherian) inference is applied to assessing the explosive damage. This technique is called statistical resampling (Efron's bootstrap) and applied as a practical, albeit not equivalent alternative to the Bayesian approaches. It is shown that statistical resampling is capable to yield confidence intervals of damage probabilities and can be applied almost automatically. It operates without using cumbersome methods of statistical inference developed in the classical statistics. The bootstrap confidence intervals do not contain any subjective information except the degree of confidence for which these intervals are computed. The degree of confidence must be chosen by the engineer. The bootstrap confidence intervals are applied to estimating damage probabilities on the basis of the small‐size sample of blast loading characteristics. An estimate of the risk of explosive damage is expressed as a set of bootstrap confidence intervals computed for damage probabilities and related outcomes of this damage.

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Section: The Need Of Uncertainty Modellingmentioning

confidence: 99%

Explosive Damage to Industrial Buildings: Assessment by Resampling Limited Experimental Data on Blast Loading

Vaidogas

2005

Journal of Civil Engineering and Management

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“…Researchers have witnessed an increasing number of studies relevant to accidental explosion, which the current study aims at. In a series of studies, Vaidogas (2002, 2006a,b,c) investigated on simulation‐based forecasting effects of an accident explosion on the road. In Vaidogas (2006a), a simulation‐based procedure intended for selection of the probabilistic models in the absence of direct statistical data on the explosive actions is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In another article, Vaidogas (2006c) presented a Bayesian approach to forecasting damage to buildings from accidental explosions on railways. Vaidogas (2002) formulated a probabilistic framework for an assessment of risk to structures resulting from accidental vapour cloud explosions (VCEs) based on the classical Bayesian approach to risk assessment. Attention is centred on the estimation of the annual probabilities of potential damages to structural systems exposed to dangers of VCEs.…”

Section: Introductionmentioning

confidence: 99%

Incidents of fire outbreaks during fuel truck accidents in Oyo State

Dare

Oke

Olanrewaju

2009

Disaster Prevention and Management: An International Journal

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PurposeIn recent years, there has been a substantial increase in the number of fuel‐truck accidents involving fire outbreaks in Oyo State, Nigeria. These accident explosions have mechanical‐induced actions on the road, with potential costly damages to structures and non‐structural property exposed to them, and loss of lives. The need to understand causes of these accident scenarios become important in order to properly plan to avoid them. The purpose of this paper is to reveal results of a survey is carried out to reveal important elements of fuel‐truck accident in Nigeria.Design/methodology/approachA survey was carried out to determine the various causes of accidents and rollover fire outbreaks in fuel trucks in Oyo State, Nigeria, using primary data collected from field and secondary data obtained from the Nigeria Police, Road Safety Commission and Fire Services Agency.FindingsFrom about 358 transport accidents recorded in Nigeria between 1999 and 2002, only 33 were due to cars while the rest involved trucks and heavy‐duty vehicles. The survey showed that about 32 per cent of truck drivers are below 30 years and probably immature. Also 62 per cent of fuel truck tanks manufactured are of inferior quality and may thus have been aiding fire outbreaks when there is an accident. The study also showed that about 54 per cent of tank leakages that may lead to fire outbreak are due to operators' carelessness.Practical implicationsThe study established the need for more education among the drivers and adequate legislation for tank manufacturers.Originality/valueThe work is perhaps the first to investigate this important area of safety research in the study area. The work would be of value to safety practitioners.

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Actions imposed on structures during man‐made accidents: Prediction via simulation‐based uncertainty propagation

Vaidogas

2005

Journal of Civil Engineering and Management

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Prediction of mechanical, thermal, and chemical actions induced during man-made accidents (accidental actions) is of crucial importance to assessing potential damage to structures exposed to these actions. A logical result of such a prediction may be expressed in the form of probabilistic models describing likelihood of occurrence and characteristics of accidental actions. For many types of accidental actions the models are to be selected under the conditions of incomplete knowledge about and/or scarce statistical information on intensities and likelihood of imposition of the actions. This paper proposes a simulation-based procedure intended for a selection of the probabilistic models under these conditions. The proposed procedure is formulated in the context of the classical Bayesian approach to risk assessment. The main idea of it is that statistical samples necessary for fitting the probabilistic action models can be acquired from a stochastic simulation of accident sequences leading to an imposition of accidental actions. Formally, the stochastic simulation of accidents serves the purpose of propagating uncertainties related to the physical phenomena capable of inducing accidental actions. These uncertainties are quantified in line with the classical Bayesian approach. The simulation-based procedure can be used for damage assessment and risk studies within the methodological framework provided by the above-mentioned approach.

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Probabilistic Framework for Assessing Risks to Structures Stemming From Accidental Explosions

Cited by 3 publications

References 7 publications

Explosive Damage to Industrial Buildings: Assessment by Resampling Limited Experimental Data on Blast Loading

Explosive Damage to Industrial Buildings: Assessment by Resampling Limited Experimental Data on Blast Loading

Incidents of fire outbreaks during fuel truck accidents in Oyo State

Actions imposed on structures during man‐made accidents: Prediction via simulation‐based uncertainty propagation

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