Behaviour of helmets during head impact in real accident cases of motorcyclists

Bourdet, Nicolas; Deck, Caroline; Tinard, Violaine; Willinger, Rémy

doi:10.1080/13588265.2011.625676

Cited by 25 publications

(10 citation statements)

References 10 publications

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“…However, the maximum is reached after 11–13 ms for lower damping values. This lag is immanent for the viscoelastic model but does not correspond to the experimental results [ 15 , 18 ] that report an immediate acceleration increase after hitting the obstacle, without any significant delay.…”

Section: Materials and Methodscontrasting

confidence: 67%

Effective Viscoplastic-Softening Model Suitable for Brain Impact Modelling

2022

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In this paper, we address the numerical aspects and implementation of a nonlinear viscoplastic model of the mechanical behaviour of brain tissue to simulate the dynamic responses related to impact loads which may cause traumatic injury. Among the various viscoelastic models available, we deliberately considered modifying the Norton–Hoff model in order to introduce non-typical viscoplastic softening behaviour that imitates a brain’s response just several milliseconds after a rapid impact. We describe the discretisation and three dimensional implementation of the model, with the aim of obtaining accurate numerical results in a reasonable computational time. Due to the large scale and complexity of the problem, a parallel computation technique, using a space–time finite element method, was used to facilitate the computation boost. It is proven that, after calibrating, the introduced viscoplastic-softening model is better suited for modelling brain tissue behaviour for the specific case of rapid impact loading rather than the commonly used viscoelastic models.

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Section: Materials and Methodscontrasting

confidence: 67%

Effective Viscoplastic-Softening Model Suitable for Brain Impact Modelling

2022

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“…The mass and inertia of all the different parts of the vehicles are gathered from the datasheet available. The contact force functions used on each part of the car are extracted Martinez et al [14], similarly as Bourdet et al [15], [16]. Coming to the simulation output data, motorcyclist's impact location and impact time were considered as well as the velocity vector of the different body segments at the instant just before impact and called impact velocity.…”

Section: Methodsmentioning

confidence: 99%

Impact conditions of motorcyclist per body segment

Bourdet

Ballestster²,

Deck

et al. 2020

International Journal of Crashworthiness

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Multy-body simulation is applied to compute the motorcyclist's kinematic in case of virtual motorcycle versus car accident. Results show that for over 50% of the simulations, the impact velocities of the whole body exceed 8 m/s when impacting the vehicle and 4 m/s at the time of road impact. The head impacts the vehicle most often with a speed exceeding 7.5 m/s on the fronto-lateral impact configuration, when thorax, abdomen and pelvis are the most exposed segments for the other impact configurations, with impact velocities beeing in the range of 10 to 15 m/s. Changes in BMI leads to minor influence on the impact velocities of the different body, especially for the first impact. As expected, body impact speed increases with the speed of the motorcycle, for both, the first and the second impacts. These results will contribute to an improvement in the evaluation and optimization of motorcyclist's protection garnement.

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“…Entre otras políticas, estrategias y contramedidas para mejorar la seguridad vial del motociclista y las recomendaciones de la WHO (2017), se deben considerar: promover la cultura y la educación en seguridad vial para motociclistas (Segui-Gomez y Lopez-Valdes, 2007); analizar los informes de accidentes en motocicletas para conocer los factores de riesgo asociados (Schneider IV et al 2012); campañas de seguridad vial en los usuarios más vulnerables en las edades de 15 a 30 años (Zambon y Hasselberg, 2006;Ivers et al, 2016); promover el uso de elementos protectores (casco, guantes, espinilleras, coderas, airbags, protectores lumbares) (Li et al, 2008;Donate-López et al, 2010;Bourdet et al, 2012); restringir y sancionar la conducción bajo la influencia de drogas y alcohol (Peek-Asa y Kraus, 1996; Albalate y Fernández-Villadangos, 2010); Controlar la velocidad según el tipo de carretera (Clabaux et al, 2012); mejorar la calidad de las carreteras o diseñar carriles exclusivos (Daniello y Gabler, 2011;Sager et al, 2014); mejorar las condiciones mecánicas y el mantenimiento (e.g. frenos ABS, neumáticos de calidad, sistema antichoque y estabilización) (Langley et al, 2000;Fowler et al, 2016;Rizzi et al, 2016); mejorar la visibilidad del motociclista (por ejemplo, con el usos de chalecos, reflectantes, o sistemas de iluminación) (Cercarelli et al, 1992., Clarke et al, 2007; mejorar las condiciones de seguridad vial, como la señalización, la iluminación y la infraestructura (Quddus et al, 2001, Haque et al 2009Schneider IV et al, 2010;Pai, 2011); prohibir el transporte y la exposición de niños en motocicletas (Brown et al, 2018); penalizar las infracciones y las conductas de riesgo (i.e.…”

Section: Análisisunclassified

Predicción de accidentes viales en Cartagena, Colombia, con árboles de decisión y reglas de asociación

Ospina-Mateus

Quintana

2019

E&R

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Prediction of road accidents in Cartagena, Colombia, using decision trees and association rulesThe main objective of this research is to predict the factors associated with the severity of road accidents in Cartagena (Colombia); the methodology is based on data mining techniques such as decision trees (J48) and association rules (support, confidence, Lift). The research was developed with 10,053 traffic accident records between 2016 and 2017, using the WEKA (Waikato Environment for Knowledge Analysis) software. In the analysis, the severity was defined as low risk (material damage), and high risk (injured and fatal victims), and its validation considered the 10-fold cross-sectional technique. Among the most significant results, it was evidenced that motorcyclists, cyclists are the most vulnerable road users, and motorcyclists between the ages of 20-39 are prone to road accidents with high severity. Finally, the road accident factors identified help promote countermeasures to improve the city's road safety.

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Behaviour of helmets during head impact in real accident cases of motorcyclists

Cited by 25 publications

References 10 publications

Effective Viscoplastic-Softening Model Suitable for Brain Impact Modelling

Effective Viscoplastic-Softening Model Suitable for Brain Impact Modelling

Impact conditions of motorcyclist per body segment

Predicción de accidentes viales en Cartagena, Colombia, con árboles de decisión y reglas de asociación

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