Full-vehicle crash simulations that account for occupant injury are essential for automobile safety assessment; however, they are computationally intensive and time-consuming. In particular, dash panel deformation plays a key role in transmitting impact loads to an occupant’s lower extremities. To address this issue, this study proposes a two-stage machine learning framework for occupant lower-limb injury assessment. In the first stage, the deformation behavior of the dash panel is predicted using a machine learning model, enabling efficient generation of a wide range of deformation patterns. In the second stage, occupant lower-limb injury metrics are evaluated based on the predicted deformation using a sled model. While the ultimate objective is to establish the complete two-stage framework, the present paper is limited to the first stage. It investigates the feasibility of machine learning-based deformation prediction. Deformation distributions of simplified structural components are predicted using an XGBoost-based machine learning model, in which principal component scores derived from geometric and deformation data serve as input features. The objective is to efficiently generate representative deformation modes from limited training data rather than optimizing prediction accuracy for individual deformation responses. Numerical experiments are conducted to investigate the effectiveness of the proposed prediction framework. The results of the proposed approach show good agreement with crash simulations in overall deformation behavior, while local deformation is not reproduced perfectly. These findings demonstrate the feasibility of machine learning-based dash panel deformation prediction as the first step toward the proposed two-stage framework for lower-limb injury assessment.
Sugiyama et al. (Tue,) studied this question.