Abstract
Forecasting mechanical actions induced by accidental explosions on the road is of crucial importance to assessing potential damage to structures and non‐structural property exposed to them. A logical result of such forecasting may be expressed in the form of probabilistic models. They should quantify likelihood of occurrence and physical characteristics of accidental explosions. Generally the models are to be selected under the conditions of sparse statistical information on intensities and likelihood of explosive actions. The first part of the present paper proposes a simulation‐based procedure intended for selection of the probabilistic models in the absence of direct statistical data on the explosive actions. The proposed procedure is formulated in the context of the classical Bayesian approach to risk assessment. The main idea of the procedure is that statistical samples necessary for fitting the probabilistic models can be acquired from a stochastic simulation of an accident involving an explosion on the road. The proposed simulation‐based procedure can be used for damage assessment and risk studies within the methodological framework provided by the above‐mentioned approach. A case study illustrating an application of the proposed procedure is given in the second part of the paper.
Highlights
Man-made accidents occurring during transportation of hazardous substances by road and rail include such adverse physical phenomena as explosions, fires, and releases of dangerous materials [13]
The present paper will be focussed on one type of accidental actions, namely, actions which can be generated by an accidental explosion (AE1) on the road
This paper proposed a computational procedure intended for selecting mathematical models for actions induced during accidental explosions (AEs) on the road
Summary
Man-made accidents occurring during transportation of hazardous substances by road and rail include such adverse physical phenomena as explosions, fires, and releases of dangerous materials [13]. The available knowledge can sometimes allow predicting accidental actions merely by mathematical modelling It can be carried out by applying a stochastic (Monte Carlo) simulation of accidents inducing accidental actions [7]. The problem considered in this paper is how to select pam .r(m) which expresses the frequency (annual probability) of exceeding the magnitude m of a mechanical effect generated by AE. This model can be schematically defined as .r(AE) × P(m | AE), where P(m | AE) is the conditional probability of exceeding m given AE. It follows from the definition (1) that a selection of the pam FrX ( x) amounts to a selection of cdf FPa ( pa |θ á ) and the family of cdfs FXi ( x |θ xi ) as well as assignment of the respective weights pi
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