Reinforced concrete (RC) walls are vulnerable to severe damage under high-intensity, close-in TNT explosions. Substantial secondary fragments at high ejecting velocities could be generated from the damaged wall, posing serious threats to people, facilities and structures in the area. Predicting the blast-induced secondary fragments remains a great challenge. Traditional computational methods, such as the finite element method (FEM) or meshfree methods, are often used to predict the fragment characteristics despite their inherent problems, such as the application of erosion and predefining the weak sections in the simulation. They also require high computational power to perform the simulation, thus limiting their use in creating an adequate dataset to thoroughly analyse the characteristics of secondary fragments and the associated threats. This study employs a recently developed machine learning-based approach named Fragment Graph Network (FGN), a variant of Graph Neural Networks (GNNs), to generate a large dataset of fragment characteristics. This FGN model can efficiently predict the fragment mass, size, and velocity with a significantly reduced computational cost. Intensive predictions of fragments from different wall configurations and explosion intensities are carried out. The results are used to develop analytical formulae for predicting secondary fragments of RC walls subjected to close-in explosions.

中文翻译：

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基于碎片图网络 （FGN） 的近距离爆炸下钢筋混凝土墙的碎片预测


钢筋混凝土 （RC） 墙在高强度、近距离 TNT 爆炸下容易受到严重损坏。受损的墙壁可能会产生大量高喷射速度的次生碎片，对该地区的人员、设施和结构构成严重威胁。预测爆炸诱导的次级片段仍然是一个巨大的挑战。传统的计算方法，如有限元法 （FEM） 或无网格法，通常用于预测碎片特性，尽管它们存在固有的问题，例如应用侵蚀和在模拟中预定义薄弱部分。它们还需要高计算能力来执行模拟，从而限制了它们在创建足够的数据集以彻底分析二级碎片的特征和相关威胁方面的用途。本研究采用了一种最近开发的基于机器学习的方法，称为片段图网络 （FGN），它是图神经网络 （GNN） 的一种变体，以生成片段特征的大型数据集。该 FGN 模型可以有效地预测片段质量、大小和速度，并显著降低计算成本。对来自不同壁型和爆炸强度的碎片进行密集预测。结果用于开发分析公式，用于预测 RC 壁的次级碎片受到近距离爆炸。