Finite element human body models have in recent years become widely used in the area of vehicle safety evaluations. They make it possible to predict injury risk in specific areas, down to the organ level in the human body. An existing human body model, SAFER HBM includes a rib cage representing an average male. However, humans have a large variability in rib geometry and material properties leading to uncertainties in non-linear phenomena such as rib fracture risk. Hence, it cannot be known if predictions based on an average male representation are applicable to other similar individuals. In simulation studies with the SAFER HBM, rib cortical bone thickness, rib cross-sectional width, and rib cortical bone material properties have been identified as the most influential for the magnitude of rib strains and thus, they have a large influence on the strain-based rib fracture risk. This means that the predicted injury outcome is sensitive to the particular rib properties of an individual, and in a real-world scenario, a distribution of injury outcomes is expected across a population. Knowledge of the injury risk distribution can aid vehicle designers in developing safer vehicles. This distribution can be found through repeated human body model simulations with various rib properties, but due to the lengthy simulation times, this is not feasible. This thesis aims to predict human body model rib strain histories, given variations in the three biomechanical parameters, rib cortical bone thickness, rib cross-section width and rib cortical bone material with the help of graph neural networks (GNNs) for both single and mixed impact scenarios. Several variations of GNNs were used and implemented with help of PyTorch and PyTorch Geometric. An extensive hyperparameter study was performed on a small part of one human body model rib, to find the optimal combinations of hyperparameters and GNNs. The data used in training and evaluation of the networks was generated in LS-DYNA with SAFER HBM v10 and post-processed in Meta post processor. To be able to generate many training examples, the HBM was subjected to a simplified impact scenario consisting of a pendulum impact to the chest. As final verification, the trained GNNs were applied to predict rib strains in a vehicle impact scenario. Evaluation of the GNNs' prediction accuracy on the whole rib cage for all impact scenarios was made by studying the root mean square error along with differences in predicted and actual peak strain, rib fracture risk, time the peak strain occurs and the euclidean distance between the locations within the rib of real and predicted peak strains. The results showed that it is possible to accurately predict strain histories. Further, a multilayer perceptron (MLP) model consistently achieved the lowest errors in all measurements for mixed impacts. However, the trained model produced slightly unexpected errors for test data extracted from vehicle simulations compared to simplified simulations. This is an indication that retraining the model on data from vehicle simulations may be necessary. In conclusion, this thesis has shown the possibility to predict strain histories from a SAFER HBM rib cage extracted from simplified simulations and simulations including the full vehicle model, the SAFER HBM and all safety systems, to investigate the effects of human variability in the rib cage.