Validated numerical unrestrained occupant-seat crash scenarios for high-speed trains integrating experimental, computational, and inverse methods.

Objective: Occupant impact safety is critical for train development. This paper proposes a systematic procedure for developing validated numerical occupant crash scenarios for high-speed trains by integrating experimental, computational, and inverse methods.

Methods: As the train interior is the most potentially injury-causing factor, the material properties were acquired by mechanical tests, and constitutive models were calibrated using inverse methods.

Assessment of Pedestrians' Head and Lower Limb Injuries in Tram-Pedestrian Collisions.

Analysis of pedestrians' head and lower limb injuries at the tissue level is lacking in studies of tram-pedestrian collisions. The purpose of this paper therefore to investigate the impact response process and severity of pedestrians' injuries in tram-pedestrian collisions, using the Total Human Model for Safety (THUMS) pedestrian human body model together with the tram FE model. Two full-scale tram-pedestrian dummy crash tests were performed to validate the FE model, and the total correlation and analysis (CORA) score of head acceleration yielded values of 0.

Assessment of standing passenger traumatic brain injury caused by ground impact in subway collisions.

Human head is the most vulnerable region in subway collisions. To design a safer subway, the head impact biomechanical response should be studied first. This paper aims to investigate the standing passenger head-ground impact dynamic response and traumatic brain injury (TBI) in subway collisions.

An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations.

Car-electric bicycle (e-bike) accidents have been the subject of strong attention due to the widespread usage of e-bikes and a high casualty rate for their riders. Manually conducted accident reconstruction is based on the trial-and-error method with a limited number of parameter combinations, which makes it time-consuming and subjective. This paper aims to develop an intelligent method for accurate, high-efficient reconstruction of accidents involving cars and e-bikes.