Thoracic injuries represent a major cause of trauma in traffic accidents, where rib impact responses critically determine crash test dummies biofidelity. Currently, there remains a performance gap between the impact responses of crash test dummy chests and human thoraxes. This study presents an intelligent optimization method for crash dummy rib design to improve thoracic biofidelity. A cumulative rib force model was developed by integrating human thorax-rib parallel spring mechanics with dummy structural characteristics, validated through impact tests and genetic algorithm-based parameter identification. Comparative analysis revealed 8.03% and 9.82% relative errors between the model-derived stiffness and damping coefficients and experimental thoracic measurements, respectively. The Kroell chest response corridor was decomposed into a single-rib channel for the crash test dummy. Structural optimization was achieved via radial basis function neural networks and whale optimization algorithm. When the structural parameters of the rib were set to Rl = 200 mm, Rw = 286 mm, Rt = 1.5 mm, Rh = 18.5 mm, r1 = 90 mm, Zh = 12.3 mm, Zg = 1.52 mm, and Za = 78°, the optimized rib exhibited a peak force of 0.480 kN and a peak displacement of 52.70 mm, both of which met the target ranges, while the hysteresis rate showed a slight deviation due to inherent material property limitations. This study has important theoretical and practical significance for improving the biomimetic performance of the crash test dummy.
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