Scaling masonry structures in a blast environment

Abstract

Masonry construction has been globally ubiquitous for centuries due to its significant compressive strength and diverse architectural potential. Despite this, masonry is vulnerable to lateral loads such as those produced by explosions. The 21st century has been marred by a growing trend and threat of terrorist incidents, many of which involve explosive detonations. These generate blast waves which transmit substantial specific impulse to masonry buildings, resulting in significant damage and hazard to the occupants and those nearby. This highlights the need for methods to assess masonry structural resilience and human hazard due to blast. Full-scale testing of masonry response to blast is challenging due to the need for a large testing site in tandem with high construction costs. The original contribution of this PhD has been to develop and experimentally benchmark a new scaling method for blast-loaded masonry built with commercially sourced materials. By adopting the concepts of dynamic similitude, a reciprocal scale factor can be applied to a reduced-scale (model) structure’s density to replicate a full-scale (prototype) structure’s lateral to vertical force ratio. Two model and prototype pairs were subjected to loading from 100kg TNT at two overpressures where results have shown that the models were able to qualitatively replicate the prototype cracking and collapse and quantitatively characterise the debris. This research has also examined blast flow and masonry response models via CFD and the Applied Element Method (AEM). CFD results demonstrated acceptable agreement with the blast trial. AEM solutions indicated an ability to model masonry cracking or debris generation, but results showed the need for additional work to develop a semi-rigid corner restraint to model both response phases sequentially. A series of future trials have been designed to test the scaling method with long duration, large impulse blasts. This trial data will permit further benchmarking of AEM to examine its predictive capabilities

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Identifiers

ORCID for Simon K Clubley: orcid.org/0000-0003-3779-242X

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