Hybrid Framework for Safety Design of Human–Rail Vehicle Transportation System Using Stochastic Approach and Optimization

Abstract

The human–rail vehicle transportation system safety design is complicated given that the complexity of the multilevel system with parameter uncertainties propagating from the vehicle structure (primary collision) to the interior human compartment (secondary collision). This study establishes a hybrid framework incorporating a stochastic approach and an integrated optimization strategy to improve train crashworthiness and reduce passenger crash injuries. The stochastic approach utilizes adaptive sparse polynomial chaos expansion models and variance-based sensitivity indices to evaluate the statistic characteristics of system responses and quantify the contribution ranking of uncertain parameters to response variations. The optimization strategy integrating the evolutionary algorithm and the multi-criteria decision making (MCDM) is proposed to solve the non-uniqueness of Pareto optimal solutions. In the optimization process, the modified DEMATEL–ANP method with interval type-2 fuzzy sets is developed to deal with vague linguistic judgments for the importance sequence of human injury responses. The q-rung orthopair trapezoidal fuzzy uncertain linguistic sets–TOPSIS method is established to address hesitant linguistic evaluations for the Pareto front and select the final optimal solution. Compared with the initial design, the driver Abbreviated Injury Scale (AIS) 3+ joint injury probability is reduced from 67.08% to 14.17% after optimization. Results prove that the proposed framework is a practical tool for improving the passive safety of railway industry.