Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis

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.