Simulation of a Typical Camel–Vehicle Collisions (CVCs) in Saudi Arabia

Al-Shammari, Naif K.; Neal-Sturgess, Clive · 2016 · Crossref

DOI: 10.51758/agjsr-1/2-2016-0006

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Summary

This study addresses the rising incidence of Camel–Vehicle Collisions (CVCs) in Saudi Arabia, a significant cause of spinal cord injuries and fatalities. Despite constituting only 1% of road traffic accidents, CVCs account for 15% of accidents resulting in spinal cord injury and one-third of cervical injuries. The research aims to identify the biomechanical mechanisms of driver spinal injuries and evaluate injury risk predictors, specifically focusing on the Peak Virtual Power of the Neck (PVPn). The researchers developed a two-dimensional multibody dynamic model using Working Model software to simulate typical CVCs in sagittal and frontal planes. The simulation integrated three systems: a vehicle model based on a Toyota Corolla, a driver dummy representing a 75 kg male with a detailed 24-vertebra spine model, and a typical adult dromedary camel weighing 700 kg. The models were validated against Insurance Institute for Highway Safety (IIHS) frontal barrier test data for the vehicle and recorded crash data for camel kinematics. Simulations were conducted at impact speeds of 27, 80, and 120 km/h for both belted and unbelted drivers. The study calculated intervertebral forces, velocities, and bending moments to determine PVPn, which was then correlated with real-world injury data from Saudi Arabia. The results demonstrated that the PVPn at each intervertebral level correlates strongly with the likelihood of neck injuries observed in real-world crashes. Statistical analysis revealed a highly significant correlation between PVPn and injury incidence. The study found that injury severity, measured by the Abbreviated Injury Scale (MAIS), increases with impact speed; for example, a 27 km/h impact resulted in a MAIS of 2, while a 120 km/h impact resulted in a MAIS of 6. Furthermore, the simulations confirmed that seat belt usage significantly reduces the risk and severity of neck injuries compared to unbelted occupants. The PVPn metric proved to be a reliable predictor of injury severity, aligning well with the "Master PVP Curve" where MAIS is linearly proportional to PVP. The significance of this work lies in establishing PVPn as a valid and effective criterion for predicting neck injuries in CVCs, a unique collision type not adequately addressed by existing injury criteria like Nij or NIC. By validating the model against real-world data, the study provides a tool for understanding injury mechanisms and evaluating countermeasures. The findings underscore the importance of seat belt usage and suggest that PVPn can be used to assess vehicle safety and occupant protection strategies specifically for large animal collisions, aiding in the development of targeted interventions to reduce the heavy losses associated with CVCs in Saudi Arabia.

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• incidence prevalence