Mathematical analysis of blast pressures has typically involved the empirical fitting of parametric models, which assumes a specific function shape. In reality, the true shape of the blast pressure is unknown and may lack a parametric form, particularly in the negative phase following arrival of the secondary shock. In this work, we develop a non-parametric (NP) representation that makes few assumptions and relies on the observed experimental data to fit a unique and previously unknown model. This differs from traditional approaches by not arbitrarily selecting a single, restrictive class of functions and estimating a minimal set of parameters, but rather estimating the underlying function class for which the blast pressure is generated; learning the model directly from the observed data. The method was applied to experimental blast measurements and the NP estimates matched the experimental data with a high degree of accuracy, both qualitatively and quantitatively. The NP approach was shown to significantly outperform other commonly used approaches, near-perfectly track the entire pressure and specific impulse history and predicting experimental peak specific impulse to within ±0.5% in all cases (compared to ±5.0% for a trained artificial neural network (ANN) and ±7.5% for the UFC semi-empirical approach). The NP approach predicts experimental net specific impulses (positive and negative phases combined) with a maximum variation of 2.7%, compared to maximum variations of −116% and 55% for the UFC and ANN approaches, respectively. Since the framework is probabilistic in nature, it can naturally account for random noise in sensor measurements, which are typically more pronounced in blast experiments than many other machine learning applications.

Numerical analysis is increasingly used for batch modelling runs, with each individual model possessing a unique combination of input parameters sampled from a range of potential values. Whilst such an approach can help to develop a comprehensive understanding of the inherent unpredictability and variability of explosive events, or populate training/validation data sets for machine learning approaches, the associated computational expense is relatively high. Furthermore, any given model may share a number of common solution steps with other models in the batch, and simulating all models from birth to termination may result in large amounts of repetition. This paper presents a new branching algorithm that ensures calculation steps are only computed once by identifying when the parameter fields of each model in the batch becomes unique. This enables informed data mapping to take place, leading to a reduction in the required computation time. The branching algorithm is explained using a conceptual walk-through for a batch of 9 models, featuring a blast load acting on a structural panel in 2D. By eliminating repeat steps, approximately 50% of the run time can be saved. This is followed by the development and use of the algorithm in 3D for a practical application involving 20 complex containment structure models. In this instance, a ∼20% reduction in computational costs is achieved.

The response of plates subjected to blast loads is of considerable scientific interest. The loading imparted to a structure following a close-in detonation of a high explosive is typically high in magnitude, near-impulsive, spatially non-uniform, with localised variability and a high dependence on factors such as charge shape, position, and composition. The resulting structural response may induce large displacements in materials whose properties may not be fully characterised. In order to properly account for the effects of such intrinsic and extrinsic uncertainties, modelling approaches must balance the competing demands of accuracy and low computational demand. This article applies the extended Hamilton's principle to rigid-plastic thin plates subjected to impulsive blast loads to derive the governing equation of motion without a prior assumption of the initial specific impulse distribution. Closed-form solutions to predict the plastic response are derived for rectangular and circular plates. The analytical models for uniform specific impulses are found to be in good agreement with high-fidelity numerical simulations performed using LS-DYNA and experimental data available in the literature.

Structural engineers are often tasked with the challenge of enhancing the resilience of buildings and structures to withstand blast loading from the detonation of energetic materials. Such loading typically occurs during terrorist attacks and from accidental explosions in nearby chemical and explosive storage facilities. However, existing methods for predicting the governing blast loading on a structure, through consideration of multiple explosion scenarios, are unsuitable for most practising structural engineers. This paper will address the need for fast-running tools for predicting blast loads on structures in the far-field by presenting the semi-empirical EMBlast method that calculates free-field and reflected pressure-time histories for both the positive and negative phase. For finite size target surfaces, this method also accounts for clearing effects, a phenomenon that results in gradually reducing the reflected pressures on the front face of a building, to the lower free-field pressures experienced by the sides and roof. To validate the predictions of the EMBlast method in the far-field, computational fluid dynamic analyses are performed over a long-range of scaled distances. Furthermore, the EMBlast predictions are also compared with the results from published blast tests, existing empirical methods and computational fluid dynamic simulations identified in the literature.

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

Detonation of a high explosive close to a structural component results in a blast load that is highly localized and nonuninform in nature. Prediction of structural response and damage due to such loads requires a detailed understanding of both the magnitude and distribution of the load, which in turn are a function of the properties and dimensions of the structure, the standoff from the charge to the structure, and the composition of the explosive. It is common to express an explosive as an equivalent mass of TNT to facilitate the use of existing and well-established semi-empirical methods. This requires calculation of a TNT equivalency factor (EF), that is, the mass ratio between the equivalent mass of TNT and the explosive mass in question, such that a chosen blast parameter will be the same for the same set of input conditions aside from explosive type. In this paper, we derive EF for three common explosives: C4, COMP-B, and ANFO, using an equivalent upper bound kinetic energy approach. A series of numerical simulations are performed, and the resultant magnitudes and distributions of specific impulse are used to derive the theoretical upper bound kinetic energy that would be imparted to a flexible target. Based on the equivalent mass of TNT of each explosive, which is required to impart the same kinetic energy for a given target size and standoff distance as of TNT, the EF is calculated. It is shown that in the near-field, the EFs are non-constant and are dependent on both standoff and target size. The results in the current study are presented in a scaled form and can be used for any practical combination of charge mass, distance from the charge to the target, target size, thickness, and density.

The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural response given data including external forces, geometry, member sizes, restraint, etc. -- characterising a forward problem (structural causalities $\to$ structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response $\to$ structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.

Many conventional high explosives do not contain sufficient internal oxygen to fully combust the gaseous products which result from detonation of the explosive material. Because of this, under-oxygenated explosives continue to burn after detonation. This process, called afterburn, is known to influence the late-time pressure and energy released by the explosive, which has particular significance for confined explosives. Recent experimental work at the University of Sheffield, along with a small number of previous studies, has shown that some afterburn occurs at timescales commensurate with the development of the shock wave. This article presents the results from a series of tests measuring the reflected pressure acting on a rigid target following the detonation of small explosive charges. High-speed video is used to capture the emerging structure of the detonation products and air shock, while the spatial and temporal distributions of the reflected pressure are recorded using an array of 17 Hopkinson pressure bars set flush with an effectively rigid target. Tests are conducted in inert atmospheres and oxygen-rich atmospheres in order to assess the contribution of rapid afterburn on the development of the shock front and interaction with a rigid target situated close to the explosive charge. The results show that early-stage afterburn has a significant influence on the reflected shock parameters in the near-field.

Near-field blast load measurement presents an issue to many sensor types as they must endure very aggressive environments and be able to measure pressures up to many hundreds of megapascals. In this respect the simplicity of the Hopkinson pressure bar has a major advantage in that while the measurement end of the Hopkinson bar can endure and be exposed to harsh conditions, the strain gauge mounted to the bar can be affixed some distance away. This allows protective housings to be utilized which protect the strain gauge but do not interfere with the measurement acquisition. The use of an array of pressure bars allows the pressure-time histories at discrete known points to be measured. This article also describes the interpolation routine used to derive pressure-time histories at un-instrumented locations on the plane of interest. Currently the technique has been used to measure loading from high explosives in free air and buried shallowly in various soils.