Deep learning-based analysis to identify fluid-structure interaction effects during the response of blast-loaded plates

IF 2.1 Q2 ENGINEERING, CIVIL International Journal of Protective Structures Pub Date : 2023-08-30 DOI:10.1177/20414196231198259
L. Lomazzi, David Morin, F. Cadini, A. Manes, V. Aune
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引用次数: 1

Abstract

Blast events within urban areas in recent decades necessitate that protective design is no longer reserved for military installations. Modern civil infrastructure composed of light-weight, flexible materials has introduced the consideration of fluid-structure interaction (FSI) effects in blast-resistant design. While the action of blast loading on massive, rigid structures in military fortifications is well established, assessment of FSI effects is, at present, only possible through computationally expensive coupled simulations. In this study, a data-driven approach is proposed to assist in the identification of the blast-loading scenarios for which FSI effects play a significant role. A series of feed-forward deep neural networks (DNNs) were designed to learn weighted associations between characteristics of uncoupled simulations and a correction factor determined by the out-of-plane displacement arising from FSI effects in corresponding coupled simulations. The DNNs were trained, validated and tested on simulation results of various blast-loading conditions and material parameters for metallic target plates. DNNs exposed to mass-per-unit-area, identified as an influential factor in quantifying FSI effects, generalised well across a range of unseen data. The explainability approach was used to highlight the driving parameters of FSI effect predictions which further evidenced the findings. The ability to provide quick assessments of FSI influence may serve to identify opportunities to exploit FSI effects for improved structural integrity of light-weight protective structures where the use of uncoupled numerical models is currently limited.
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基于深度学习的分析,以识别冲击载荷板响应过程中的流体-结构相互作用效应
近几十年来,城市地区发生的爆炸事件使防护设计不再局限于军事设施。现代民用基础设施由轻质、柔性材料组成,在抗爆设计中引入了流固耦合效应的考虑。虽然爆炸载荷对军事防御工事中大型刚性结构的作用已经很好地建立起来,但目前只能通过计算昂贵的耦合模拟来评估FSI效应。在这项研究中,提出了一种数据驱动的方法来帮助识别爆炸加载场景,其中FSI效应发挥了重要作用。设计了一系列前馈深度神经网络(dnn)来学习非耦合模拟特征与相应耦合模拟中由FSI效应引起的面外位移确定的校正因子之间的加权关联。基于不同爆炸载荷条件和金属靶板材料参数的模拟结果,对dnn进行了训练、验证和测试。暴露在单位面积质量下的dnn,被认为是量化FSI效应的一个影响因素,可以很好地概括一系列看不见的数据。利用可解释性方法突出了FSI效应预测的驱动参数,进一步证明了研究结果。提供FSI影响的快速评估能力可能有助于确定利用FSI效应改善轻型防护结构完整性的机会,目前非耦合数值模型的使用受到限制。
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来源期刊
CiteScore
4.30
自引率
25.00%
发文量
48
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