Forecasting Impact Injuries of Unrestrained Occupants in Railway Vehicle Passenger Compartments
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Objective: In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work.
Methods: The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm–back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation–artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods.
Results: All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ±6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ±5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ±6.3 percent and the ARE values did not exceed 3.8 percent.
Conclusions: The injury forecast model established in this article reduced repeat experiment times and improved the design efficiency of the internal compartment's structure parameters, and it provided a new way for assessing the safety performance of the interior structural parameters in existing, and newly designed, railway vehicle compartments.