Efficient Approximate Methods for Predicting Behaviors of Steel Hat Sections Under Axial Impact Loading

Hat sections made of steel are frequently encountered in automotive body structural components such as front rails. These components can absorb significant amount of impact energy during collisions thereby protecting occupants of vehicles from severe injury. In the initial phase of vehicle design, it will be prudent to incorporate the sectional details of such a component based on an engineering target such as peak load, mean load, energy absorption, or total crush, or a combination of these parameters. Such a goal can be accomplished if efficient and reliable data-based models are available for predicting the performance of a section of given geometry as alternatives to time-consuming and detailed engineering analysis typically based on the explicit finite element method. In the present study, the effectiveness of data-based approaches based on polynomial hyper-space, radial basis function and a generalized regression neural network is studied for predicting peak and mean loads for steel hat sections subjected to axial impact. Additionally, the first two approaches are applied to the prediction of time history of axial impact load. The database used in the formulation of the stated models has been generated with the aid of a validated finite element modeling procedure for explicit analysis using a standard code.