Prediction of blast loading in an internal environment using artificial neural networks

Adam A Dennis,Jordan J Pannell,Samuel E Rigby,Danny J Smyl

doi:10.1177/2041419620970570

Adam A Dennis, Jordan J Pannell + Show 2 more

Open Access

https://doi.org/10.1177/2041419620970570

Copy DOI

Abstract

Explosive loading in a confined internal environment is highly complex and is driven by nonlinear physical processes associated with reflection and coalescence of multiple shock fronts. Prediction of this loading is not currently feasible using simple tools, and instead specialist computational software or practical testing is required, which are impractical for situations with a wide range of input variables. There is a need to develop a tool which balances the accuracy of experiments or physics-based numerical schemes with the simplicity and low computational cost of an engineering-level predictive approach. Artificial neural networks (ANNs) are formed of a collection of neurons that process information via a series of connections. When fully trained, ANNs are capable of replicating and generalising multi-parameter, high-complexity problems and are able to generate new predictions for unseen problems (within the bounds of the training variables). This article presents the development and rigorous testing of an ANN to predict blast loading in a confined internal environment. The ANN was trained using validated numerical modelling data, and key parameters relating to formulation of the training data and network structure were critically analysed in order to maximise the predictive capability of the network. The developed network was generally able to predict specific impulses to within 10% of the numerical data: 90% of specific impulses in the unseen testing data, and between 81% and 87% of specific impulses for data from four additional unseen test models, were predicted to this accuracy. The network was highly capable of generalising in areas adjacent to reflecting surfaces and as those close to ambient outflow boundaries. It is shown that ANNs are highly suited to modelling blast loading in a confined internal environment, with significant improvements in accuracy achievable if a robust, well distributed training dataset is used with a network structure that is tailored to the problem being solved.

Highlights

BackgroundRecent high-profile terrorist incidents, such as the Manchester Arena bombing (2017, 22 fatalities) and the Brussels Airport attacks (2016, 33 fatalities), involved the use of high explosives detonatedDepartment of Civil and Structural Engineering, University of Sheffield, Sheffield, UKInternational Journal of Protective Structures 12(3)in a crowded internal environment (Ben-Ezra et al, 2017; Kwon et al, 2017)
Whilst the payload from vehicle-borne improvised explosive devices (VBIEDs) is considerably larger than those from person-borne explosives, the effects of a VBIED attack can be mitigated by enforcing a safe standoff distance through the use of hardened security checkpoints and anti-vehicle barriers (Cormie et al, 2009)
This study presents an evaluation of artificial neural networks (ANNs) for predicting the complex blast loading in a confined internal environment

Summary

Background

Recent high-profile terrorist incidents, such as the Manchester Arena bombing (2017, 22 fatalities) and the Brussels Airport attacks (2016, 33 fatalities), involved the use of high explosives detonated. Predictions generated by the ANNs are directly compared to numerical data from 72 different explosive scenarios during the training process This allows for a comprehensive evaluation of the network performance including an assessment of how the network’s accuracy is dependent on each of the input values. The progression of experimental techniques and bespoke modelling methods enable the use of ANNs for solving complex non-linear problems, especially those where the functional relationship between the input variables and output parameters is unknown (Alizadeh et al, 2017) They have applications involving data mapping, regression, classification and image processing (Dogan et al, 2017; Lee et al, 2012). The number of training patterns used, the complexity of the problem being modelled, and the network architecture will all influence the accuracy of a trained network because each of these factors control its ability to generalise from the training dataset (Remennikov and Rose, 2007)

Literature review

Findings

Summary and conclusion