Explosive damage to industrial buildings: assessment by resampling limited experimental data on blast loading

Abstract

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.