Inference of Blast Wavefront Parameter Uncertainty for Probabilistic Risk Assessment

Campidelli, Manuel; Tait, Michael J.; El‐Dakhakhni, Wael; Mekky, Waleed

doi:10.1061/(asce)st.1943-541x.0001299

Cited by 18 publications

(13 citation statements)

References 21 publications

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“…The substantial variability of the blast parameters has been noted by various researchers even for identical field experiments. In their work, Netherton and Stewart (2010), Bogosian et al 2002, Campidelli et al (2015), and Twisdale et al (1993) showed that in some cases, the peak overpressure and positive impulse can differ by more than 40% (and the positive duration by more than 60%) in relation to the proposed values by Kingery and Bulmash (1984). Their analyses utilized results Table 3.…”

Section: Sensitivity Analysis and Trendsmentioning

confidence: 89%

Analysis of the blast wave decay coefficient using the Kingery–Bulmash data

Karlos

Solomos

Larcher

2016

International Journal of Protective Structures

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A growing interest for the design of structures to sustain blast-induced loads has been observed in recent years as a result of the worldwide rise of terrorist bombing attacks. The blast loading is usually characterized by a sudden increase in the pressure followed by an exponential decay. The parameters of this pressure pulse are essential for design and can be found in various blast design manuals available in the open literature. One of the most widely used sources is a technical report by Kingery-Bulmash, which provides values for many blast parameters in diagrams and polynomial form. However, it does not include an equation for calculating the blast wave decay coefficient, necessary for constructing the pressure-time history of an explosion at a certain point. In this study, a review of the technical literature that contains expressions for the blast pressure decay coefficient is performed, and relevant comparisons have been made. New equations describing the decay coefficient of the Friedlander equation for both incident and reflected cases for free-air and surface bursts are proposed. These equations express the decay coefficient in terms of the scaled distance and are not valid for close-in detonations. They are entirely based on the Kingery-Bulmash data, and their accuracy is satisfactorily checked against new experimental results and their trends assessed through a sensitivity analysis. Accordingly, the positive phase of the pressure-time curve at a point can be reliably and efficiently generated.

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Section: Sensitivity Analysis and Trendsmentioning

confidence: 89%

Analysis of the blast wave decay coefficient using the Kingery–Bulmash data

Karlos

Solomos

Larcher

2016

International Journal of Protective Structures

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“…Consequently, the University of Newcastle (UoN) has undertaken explosive field trials with a view to capture blast load data that is better suited in terms of characterising ME and blast load variability. Information from tests such as these is then available for a variety of structural engineering applications, such as calculating reliability-based design load factors for explosive blast loading (Stewart, 2018; Stewart and Netherton, 2015), determining partial factors for the major wavefront parameters of reflected shock wavefronts (Campidelli et al, 2015) or estimating the probability of damage and casualty risks for infrastructure subject to explosive blast loading (e.g. Alterman et al, 2019; Shi and Stewart, 2015; Stewart, 2019).…”

Section: Introductionmentioning

confidence: 99%

Observed airblast variability and model error from repeatable explosive field trials

Stewart

Netherton

Baldacchino³

2019

International Journal of Protective Structures

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Explosive field trials have been conducted to measure the peak incident pressure, impulse and time of positive phase duration following the detonation of 15 different masses of the Plastic Explosive No #4. A novel aspect of these field trials was the repeatability of tests. Eight pressure gauges collected data during each blast, and at each scaled distance. In all, 4 blasts were conducted for each scaled distance (i.e. up to 32 measurements recorded for each scaled distance) – 60 blasts were fired in total. Consequently, this repeatability of testing allowed the mean and variance of blast pressure–time histories to be quantified, with a view to better characterise the variability of a blast itself and model error variability. This article describes the explosive field trials, and the statistical analysis of blast load variability and model error for peak incident pressure, impulse and time of positive phase duration. It was found that the mean model error is close to unity with a coefficient of variation of up to 0.15 for pressure and 0.21 for impulse. The lognormal probability distribution best fits the model error data. The probabilistic models derived from these tests can be used for a variety of structural engineering applications, such as calculating reliability-based design load or partial safety factors for explosive blast loading, and estimating the probability of damage and casualties for infrastructure subject to explosive blast loading. This is illustrated for a terrorist explosive scenario involving a spherical free-air burst, where the damage modes of interest are breaching and spalling of a concrete slab. It was found that the variability of charge mass, range and model error have a significant effect on reliability-based design.

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“…In addition, Von Rosen et al (2005) summarized the blast consequences as the repair cost, equipment/data loss, occupant/operation temporary moving cost, and the cost of casualties for a rapid blast screening estimate. Although several parameters may influence the response of different elements to blast (Campidelli et al 2015b;Netherton and Stewart 2010), it is well understood (Baker et al 1991;Olmati et al 2016) that the scaled distance (Z), afunction of the charge mass (W) and the standoff distance (R), is considered the main parameter governing the blast DBH level. Z, based on the Hopkinson-Cranz law scaling law (Baker et al 1991), can be calculated using Eq.…”

Section: Demonstration Application Of the Pra Frameworkmentioning

confidence: 99%

“…The developed subroutine facilitates the iterative blast simulation considering the input DBH parameter (I) uncertainty using the method of moments (MathWorks 2014). In the current case study, the uncertainty in I is generated following the normal distribution fit proposed by Campidelli et al (2015b) [mean value of 1.01 and coefficient of variation (COV) of 0.19] The subroutine starts by calculating I across the facade facing the blast through a 100 × 100 element grid considering the clearing effect (Campidelli et al 2015b;USDOD 2014). Fig.…”

Section: Analysis Approachmentioning

confidence: 99%

Probabilistic Resilience-Guided Infrastructure Risk Management

et al. 2020

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The increased frequency and magnitude of natural and anthropogenic hazard events that affected infrastructure systems over the past two decades have highlighted the need for more effective risk management strategies. Such strategies are expected to not only manage the immediate disruption to system's functionality following hazard realization, but to also mitigate the latter's extended-term consequences (e.g., recovery cost and restoration time), which would otherwise be disastrous. To yield realistic managerial insights, such resilience-guided risk management necessitates accounting for the different sources of uncertainties associated with both the hazard quantification and the response of the infrastructure being considered. Through considering such uncertainties, the probabilistic resilience quantification framework developed in this study is expected to provide valuable managerial insights to guide resource allocations for both pre-and posthazard realization. The applicability of the framework is demonstrated on a simplified system subjected to different anthropogenic hazard scenarios. Beyond the presented case study, the developed framework lays the foundation for adopting probabilistic resilience quantification to guide the next-generation risk management processes of infrastructure systems under different forms of natural and anthropogenic hazards.

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Inference of Blast Wavefront Parameter Uncertainty for Probabilistic Risk Assessment

Cited by 18 publications

References 21 publications

Analysis of the blast wave decay coefficient using the Kingery–Bulmash data

Analysis of the blast wave decay coefficient using the Kingery–Bulmash data

Observed airblast variability and model error from repeatable explosive field trials

Probabilistic Resilience-Guided Infrastructure Risk Management

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