On the Assessment of Reinforced Concrete (RC) Walls under Contact/Near-Contact Explosive Charges: A Deep Neural Network Approach

Abstract

In recent years, the use of machine learning has been expanded to several fields, with promising advances in structural engineering applications. Deep neural network models have been implemented to predict the structural response of systems under conventional loading. Some of those neural network models are based on datasets containing images, test data, and/or data produced by using finite element models developed for a specific environment. While the accuracy of these models relies on the size and quality of the dataset, their use for blast analysis is rather limited, as publicly available data are scarce or restricted. Reinforced concrete (RC) walls or slabs under blast loading are commonly evaluated for flexural and shear behaviour, for which performance guidelines are widely available. While such response mechanisms are typically associated with the far-field range, the target response is controlled by local failure modes when blast loads are generated by contact or near-contact detonations. This paper introduces the implementation of a neural network model for the response prediction of RC walls subjected to contact and near-contact explosions. The model predicts the damage category (i.e., no damage, spall, and breach) associated with a given explosion scenario. The model is trained using experimental data from multiple test programmes available in open-source literature. It considers several parameters associated with the explosive charge (e.g., type, geometry, charge weight, and standoff) and RC target (e.g., material properties, geometry, and reinforcement). The model is able to accurately predict 81% of the total breached specimens, 66% of the total spalled specimens, and 71% of the full set of non-damaged specimens, with an overall accuracy of 72%, with precision and recall ranging from 60 to 76% and 66 to 81%, respectively. The current model is shown to be a significantly better predictor of the damage category than the semi-empirical approach outlined in UFC 3-340-02, making it a promising tool that can be improved with the inclusion of more experimental data.