Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

Blast–obstacle interaction is a complex, multi-faceted problem. Whilst engineering-level
tools exist for predicting blast parameters (eg peak pressure, impulse and loading duration) …

In this paper, we propose an innovative machine learning approach for predicting
overpressure wave propagation generated by Boiling Liquid Expanding Vapor Explosion …

In this paper, we present a rigorous comparative study to assess and identify the most
effective machine learning model for blast loading prediction. Blast loads are known to …

Accurate prediction of blast loading from accidental hydrogen-air cloud explosion is critical
for the planning, design, and operation of the hydrogen industry. This study proposes an …

Abstract Boiling Liquid Expanding Vapour Explosions (BLEVEs) are extreme explosions
driven by nonlinear physical processes associated with explosively expanded vapour and …

Machine learning (ML) methods are becoming more prominent in blast engineering
applications, with their adaptability to new scenarios and rapid computation times providing …

A significant amount of scientific effort has been dedicated to measuring and understanding
the effects of explosions, leading to the development of semi-empirical methods for rapid …

Machine learning offers the potential to enable probabilistic-based approaches to
engineering design and risk mitigation. Application of such approaches in the field of blast …

The effects of blast waves and their consequent damage to structures have been an
increasingly popular research topic in the past decade. Various methods are used in blast …