Inverse analysis of explosives based on crater size

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• 作者：X. Zhou ; C. Bai ; Y. Zhang ; Z. Wang
• 关键词：Explosive source ; Inverse analysis ; Craters ; Soil ; Generalized regression neural network
• 刊名：Shock Waves
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：27
• 期：1
• 页码：27-35
• 全文大小：
• 刊物类别：Physics and Astronomy
• 刊物主题：Engineering Thermodynamics, Heat and Mass Transfer; Fluid- and Aerodynamics; Engineering Fluid Dynamics; Thermodynamics; Acoustics; Condensed Matter Physics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-2153
• 卷排序：27

文摘

During the investigation of terrorist attacks and other intentional explosion events, it is important to grasp the depth of a burst and the mass of the explosive used in attacks as soon as possible. The above explosion information can be rapidly obtained from the crater generated by blast waves through examining the location and dimensions of the crater. First, a brief review of methods for crater size prediction is presented. Then a series of explosion experiments under the ground or on the soil surface are conducted with different amounts of explosive. We propose the inverse analysis method of the feature parameters of explosive sources based on the crater sizes. Finally, in order to validate the inverse analysis method and prove its ability to model the non-linear relationship between crater dimensions and feature parameters of explosive sources, we compare the modeling results with the results calculated with empirical equations and the experimental results, respectively. The mass of the explosives obtained with Generalized Regression Neural Network was consistent with that used in the experiments in the soils.