Abstract A prominent threat to key structures and components in critical facilities, especially during a tornado or hurricane, is impact by wind-borne projectiles. Identifying uncertain variables in these phenomena is key for developing cost-effective design formulae and efficient reliability analysis. This task however is challenging due to the scarcity of data and the large set of uncertain factors that contribute to the impact phenomenon. The present study performs a robust global sensitivity analysis (GSA) using Sobol's indices on the response of a concrete panel impacted by a Schedule 40 pipe. A high-fidelity, nonlinear Finite Element (FE) model is developed using the Smooth Particle Hydrodynamics (SPH) formulation in LS-DYNA. The developed model was validated with the experiments conducted by the Electric Power Research Institute (EPRI). Due to the high computational demand of the SPH model, a machine learning-based surrogate model called Bayesian Additive Regression Trees (BART) is applied to emulate the computational model. This surrogate model that is trained with a limited number of generated SPH simulations is capable of accurately predicting the behavior of concrete panels subjected to projectile impact over the space of predictors. The predictors here include uncertain variables associated with concrete and steel material properties. GSA is subsequently performed by integrating the constructed surrogate model into Sobol's algorithm. Results indicate that concrete tensile strength plays a significant role in panel damage, while concrete mass density and compressive strength and mass density of the steel pipe are also significant. These findings suggest that the development of new empirical design formulae and experimental studies on the impact of concrete panels should include the identified significant variables.

中文翻译：

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影响受风载弹丸影响的混凝土板性能的重要变量：全球敏感性分析

摘要 风载弹丸的影响对关键设施中的关键结构和部件构成突出威胁，尤其是在龙卷风或飓风期间。识别这些现象中的不确定变量是开发具有成本效益的设计公式和有效的可靠性分析的关键。然而，由于数据稀缺以及导致撞击现象的大量不确定因素，这项任务具有挑战性。本研究使用 Sobol 指数对受附表 40 管道影响的混凝土面板的响应进行了稳健的全球敏感性分析 (GSA)。使用 LS-DYNA 中的平滑粒子流体动力学 (SPH) 公式开发了高保真非线性有限元 (FE) 模型。开发的模型通过电力研究所 (EPRI) 进行的实验进行了验证。由于 SPH 模型的高计算需求，应用称为贝叶斯加性回归树 (BART) 的基于机器学习的替代模型来模拟计算模型。这种使用有限数量的生成 SPH 模拟训练的替代模型能够准确预测混凝土板在预测器空间上受到弹丸撞击的行为。此处的预测变量包括与混凝土和钢材特性相关的不确定变量。随后通过将构建的代理模型集成到 Sobol 算法中来执行 GSA。结果表明，混凝土抗拉强度在面板损坏中起重要作用，而混凝土质量密度和钢管的抗压强度和质量密度也很重要。