THE EFFECT OF ANTHROPOMETRIC PARAMETERS ON THE SEAT TRANSMISSIBILITY DURING WHOLE-BODY VIBRATION: A HYBRID MODELING APPROACH BASED ON ARTIFICIAL NEURAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS

The modeling of the seat transmissibility is necessary to advance the understanding of the dynamic interactions between compliant seats and occupants. Within this investigation, an optimized artificial neural network (ANN) model was employed to clarify contributions associated with anthropometric parameters to the seat transmissibility. Anthropometric parameters underwent dimensionality reduction through the principal component analysis, and resultant principal components served as input features for the ANN model. Additionally, the ANN structure's weights and biases values were adjusted using the genetic algorithm (GA), resulting in a PCA-GA-ANN model for the prediction of seat transmissibilities. The results indicated root mean square error (RMSE) values for predicting vertical in-line and horizontal cross-axis transmissibilities from the developed model were 0.061 and 0.055, respectively, demonstrating superior effectiveness in the prediction error and trends when compared with both the ANN and GA-ANN models. The seat transmissibility predicted from the PCA-GA-ANN model exhibited resonance behaviors similar to that observed in the whole-body vibration test. The sensitivity analysis showed that the subject's age was the most predominant anthropometric parameter for the prediction, followed by gender and body mass index. The ANN model optimized with principal component analysis (PCA) and GA effectively eliminates the redundant information of anthropometric parameters, enhancing the generalization of the seat transmissibility prediction.