Development and validation of a generic finite element vehicle buck model for the analysis of driver rib fractures in real life nearside oblique frontal crashes

Iraeus, Johan; Lindquist, Mats

doi:10.1016/j.aap.2016.06.020

Cited by 21 publications

(14 citation statements)

References 21 publications

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“…Occupant age and NASS/CDS-estimated change in velocity (Delta-v, as calculated by WinSmash) were considered as covariates. In the original analysis ( Iraeus and Lindquist (2016) ) vehicle instrument panel intrusion was also found to be a significant covariate. However, when compared to the simulations the intrusion was set to zero (both in the NASS/CDS regression model and in the simulations).…”

Section: Methodsmentioning

confidence: 94%

“…The method, including the National Automotive Sampling System Crashworthiness Data System (NASS/CDS) reference risk curves, is described in detail in Iraeus and Lindquist (2016) but is briefly described here. Two datasets from the NASS/CDS database were defined, one including frontal crashes (first analyzed in Iraeus and Lindquist, 2016 ) and one including side impacts (first analyzed in Pipkorn et al, 2019 ). Both datasets included both injured and uninjured occupants.…”

Section: Methodsmentioning

confidence: 99%

“…Next, two stochastic simulation studies, one frontal and one lateral, were defined as described in Pipkorn et al (2019) . For both studies, the SAFER HBM version 9 ( Iraeus and Pipkorn, 2019 ; Pipkorn et al, 2019 ) was positioned in a parameterized finite element model of a vehicle interior ( Iraeus and Lindquist, 2016 ). For frontal impacts, the vehicle model included a driver airbag, a load-limited seat belt, and dashboard and floor pan intrusion modeling.…”

Section: Methodsmentioning

confidence: 99%

“…The study consisted of 1,000 frontal impact simulation models and 100 lateral impact simulation models. More details about the method can be found in Iraeus and Lindquist (2016) .…”

Section: Methodsmentioning

confidence: 99%

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Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models

Larsson

Blennow

Iraeus

et al. 2021

Front. Bioeng. Biotechnol.

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To evaluate vehicle occupant injury risk, finite element human body models (HBMs) can be used in vehicle crash simulations. HBMs can predict tissue loading levels, and the risk for fracture can be estimated based on a tissue-based risk curve. A probabilistic framework utilizing an age-adjusted rib strain-based risk function was proposed in 2012. However, the risk function was based on tests from only twelve human subjects. Further, the age adjustment was based on previous literature postulating a 5.1% decrease in failure strain for femur bone material per decade of aging. The primary aim of this study was to develop a new strain-based rib fracture risk function using material test data spanning a wide range of ages. A second aim was to update the probabilistic framework with the new risk function and compare the probabilistic risk predictions from HBM simulations to both previous HBM probabilistic risk predictions and to approximate real-world rib fracture outcomes. Tensile test data of human rib cortical bone from 58 individuals spanning 17–99 years of ages was used. Survival analysis with accelerated failure time was used to model the failure strain and age-dependent decrease for the tissue-based risk function. Stochastic HBM simulations with varied impact conditions and restraint system settings were performed and probabilistic rib fracture risks were calculated. In the resulting fracture risk function, sex was not a significant covariate—but a stronger age-dependent decrease than previously assumed for human rib cortical bone was evident, corresponding to a 12% decrease in failure strain per decade of aging. The main effect of this difference is a lowered risk prediction for younger individuals than that predicted in previous risk functions. For the stochastic analysis, the previous risk curve overestimated the approximate real-world rib fracture risk for 30-year-old occupants; the new risk function reduces the overestimation. Moreover, the new function can be used as a direct replacement of the previous one within the 2012 probabilistic framework.

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Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The study consisted of 1,000 frontal impact simulation models and 100 lateral impact simulation models. More details about the method can be found in Iraeus and Lindquist (2016) .…”

Section: Methodsmentioning

confidence: 99%

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Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models

Larsson

Blennow

Iraeus

et al. 2021

Front. Bioeng. Biotechnol.

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“…Another reason is that it is difficult to obtain data for the patient’s thoracic condition before the fracture, precluding comparisons of the status before and after rib fracture. Some scholars have used computer finite element analysis of rib fractures and achieved excellent results [ 10 ]. The purpose of this study was to explore the effect of displacement of the rib fracture and thoracic collapse on the thoracic volume by using normal chest CT data.…”

Section: Introductionmentioning

confidence: 99%

Assessment of thoracic volume changes after the collapse of lateral rib fractures based on chest computed tomography data: computer simulation and a multiple variable linear regression analysis

Chen

Liu

Guo³

et al. 2020

J Cardiothorac Surg

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Background Chest blunt trauma (CBT) and the resultant rib fractures often lead to thoracic collapse. The purpose of this study was to explore the effect of displacement of the rib fracture and thoracic collapse on the thoracic volume by using normal chest CT data. Methods In this retrospective study, seven consecutive normal participants were selected from our hospital between June and July 2018. Normal thoracic models were reconstructed, followed by simulation of lateral fractures through the 4th to 9th ribs under three collapse modes with 1–5 cm of collapse. The thoracic collapse models ( n = 630) were reconstructed using 3Dmax 2014. We calculated the thoracic volume and reduction percentage for each thoracic collapse model. Linear regression-based comparisons of thoracic volume reductions were performed. Results In all three collapse modes, the degree of the collapse was linearly correlated with the mean thoracic volume reduction. The reduction percentage in the posterior collapse mode was higher than that in the anterior collapse mode ( P < 0.001). The largest volume reductions in the anterior, posterior, and simultaneous collapse models were in the 6th rib fracture model ( P < 0.001), 8th rib fracture model ( P < 0.001), and 7th rib fracture model ( P < 0.001), respectively. Conclusions The influences of rib fracture displacement and collapse on the thoracic volume in the 6th through 8th ribs are critical in lateral rib fractures. For patients with 6th to 8th rib fractures and posterior rib collapse, surgical intervention to restore thoracic volume may be more essential.

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Occupant injuries in light passenger vehicles—A NASS study to enable priorities for development of injury prediction capabilities of human body models

Pipkorn

Iraeus

Lindkvist

et al. 2020

Accident Analysis & Prevention

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Development and validation of a generic finite element vehicle buck model for the analysis of driver rib fractures in real life nearside oblique frontal crashes

Cited by 21 publications

References 21 publications

Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models

Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models

Assessment of thoracic volume changes after the collapse of lateral rib fractures based on chest computed tomography data: computer simulation and a multiple variable linear regression analysis

Occupant injuries in light passenger vehicles—A NASS study to enable priorities for development of injury prediction capabilities of human body models

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