ACCIDENTAL EXPLOSIONS ON THE RAILWAY: SIMULATION‐BASED PREDICTION OF DAMAGE TO NEARBY BUILDINGS

A procedure for estimating potential damage to buildings induced by accidental explosions on the railway is developed. By the damage failures of nearby structures due to actions generated by the accidental explosions are meant. This damage is measured in terms of probabilities of potential failures caused by explosions. The estimation of the damage probabilities is based on stochastic simulation of railway accidents involving an explosion. The proposed simulation‐based procedure quantifies epistemic (state‐of‐knowledge) uncertainties in the damage probabilities. These uncertainties are expressed in terms of Bayesian prior and posterior distributions. The foundation of the procedure is a computer intensive method known as the Bayesian bootstrap. It is used for approximating the posterior distributions of damage probabilities. The application of the Bayesian bootstrap makes the proposed procedure highly automatic and convenient for assessing structures subjected to the hazard of the accidental actions. In addition, it can be used for specifying safe distances between the railway and nearby buildings. Structures of these buildings can be designed for tolerable probabilities of failures induced by accidental explosions.

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ACTIONS IMPOSED ON STRUCTURES DURING MAN‐MADE ACCIDENTS: PREDICTION VIA SIMULATION‐BASED UNCERTAINTY PROPAGATION

Journal of Civil Engineering and Management ◽

10.3846/13923730.2005.9636354 ◽

2005 ◽

Vol 11 (3) ◽

pp. 225-242

Author(s):

Egidijus R. Vaidogas

Keyword(s):

Stochastic Simulation ◽

Bayesian Approach ◽

Probabilistic Models ◽

Uncertainty Propagation ◽

Main Idea ◽

Simulation Based ◽

Probabilistic Action ◽

Potential Damage ◽

Physical Phenomena ◽

Logical Result

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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