A Hybrid Simulation-Physical Data-Driven Framework for Occupant Injury Prediction in Vehicle Underbody Structures

One major challenge in optimizing vehicle underbody structures for blast protection is the trade-off between the high cost of physical tests and the limited accuracy of simulations. We introduce a predictive framework that is co-driven by limited physical measurements and systematically augmented simulation datasets. The main problem arises from the complex components of blast impact signals, which makes it difficult to augment the load signals for finite element simulations when only extremely small sample sets are available. Specifically, a small-scale data-augmentation model within the wavelet domain based on a conditional generative adversarial network (CGAN) was designed. Real-time perturbations, governed by cumulative distribution functions, were introduced to expand and diversify the data representations for enhanced dataset enrichment. A predictive model based on Gaussian process regression (GPR) that integrates physical experimental data with augmented data wavelet characteristics is employed to estimate injury indices, using wavelet scale energies reduced via principal component analysis (PCA) as inputs. Cross-validation shows that this hybrid model achieves higher accuracy than using simulations alone. Through the case study, the model demonstrates that increased hull angle and depth can effectively reduce occupant injury.