A feasible identification method of uncertainty responses for vehicle structures

Many unavoidable uncertainties in the engineering structure will affect its performance response. It is necessary to analyze the uncertain responses generated by uncertain factors in the vehicle design. Therefore, this study proposes a feasible identification method of uncertainty responses for vehicle structures. The proposed method consists of radial basis function neural network (RBFNN) and Taylor interval expansion (IE) model. The optimal network parameters of RBFNN are trained by the improved K-means clustering algorithm and singular value decomposition (SVD). Here, the first-order and second-order differential equations are derived from the RBFNN parameters since it is difficult to calculate the partial derivatives of complex systems without explicit expressions. A series of typical functions are tested and the results show that the trained RBFNN parameters can approximate the first-order and second-order partial derivatives of different types of functions. Besides, the subinterval expansion method is further applied to improve the calculation accuracy of uncertain structural responses. Finally, the proposed method is applied to four engineering applications (vehicle hood, mechanical claw, anti-collision structures, and multi-link suspension). Compared with genetic algorithm (GA) and Monte Carlo simulation (MCS), the proposed method can improve computational efficiency while ensuring accuracy. The identification method of interval uncertainty responses can be used as an alternative for uncertainty analysis and subsequent reliability or robustness optimization.