方向编码神经网络:一种机器学习方法,用于快速预测障碍物环境中的爆炸载荷

IF 2.1 Q2 ENGINEERING, CIVIL
Adam A Dennis, S. Rigby
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引用次数: 1

摘要

机器学习(ML)方法在爆破工程应用中越来越突出,与经验方法和基于物理的方法相比,它们对新场景的适应性和快速计算时间分别提供了关键优势。然而,通常用于爆破分析的ML方法定期提供与领域特定参数相关的输入,这限制了它们在初始问题集之外的使用,并降低了它们的通用性。本文介绍了“方向编码神经网络”(DeNN);一种构造人工神经网络(ANN)以预测阻塞环境中的爆炸载荷的新方法。每个兴趣点(POI)由其周围环境的接近度和冲击波的最短传播路径来表示,以便启动网络来学习问题的基本物理。此外,定制的波浪反射方程在每个点周围创建了一个影响区,因此只有当障碍物会改变波浪的路径时,它们才会被捕捉到网络的输入中。研究表明,与基于物理的数值模型的结果相比,DeNN可以预测任何形状或大小的看不见的复杂域的峰值超压,平均绝对误差为~5 kPa,数值模型的求解时间为DeNN的30倍。该网络用于绘制内部环境中烈性炸药爆炸后可能造成的人类伤害的地图,93%以上的未发现测试点的鼓膜破裂水平都得到了正确预测。因此,它非常适合用于基于风险的概率分析,由于计算成本过高,这些分析目前不切实际。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Direction-encoded Neural Network: A machine learning approach to rapidly predict blast loading in obstructed environments
Machine learning (ML) methods are becoming more prominent in blast engineering applications, with their adaptability to new scenarios and rapid computation times providing key benefits when compared to empirical methods and physics-based approaches, respectively. However, ML approaches commonly used for blast analyses are regularly provided with inputs relating to domain-specific parameters, restricting their use beyond the initial problem set and reducing their generality. This article presents the ‘Direction-encoded Neural Network’ (DeNN); a novel way to structure an Artificial Neural Network (ANN) to predict blast loading in obstructed environments. Each point of interest (POI) is represented by the proximity to its surroundings and the shortest travel path of the blast wave in order to prime the network to learn the underlying physics of the problem. Furthermore, a bespoke wave reflection equation creates a zone of influence around each point so that obstacles are only captured in the network’s inputs if they would alter the path of the wave. It is shown that the DeNN can predict peak overpressures with mean absolute errors ∼5 kPa for unseen, complex domains of any shape or size, when compared to the results from physics-based numerical models with ∼30 times the solution time of the DeNN. The network is used to develop maps of likely human injury following detonation of a high explosive in an internal environment, with eardrum rupture levels being correctly predicted for over 93% of unseen test points. It is therefore highly suited for use in probabilistic, risk-based analyses which are currently impractical due to excessive computational cost.
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来源期刊
CiteScore
4.30
自引率
25.00%
发文量
48
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