Analysis of a Dolly Rollover with PC-Crash

Rose, Nathan A.; Beauchamp, Gray

doi:10.4271/2009-01-0822

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…is software is primarily developed to take accident reconstruction and has been used for collisions between vehicles [34] and accidents involving vehicles and pedestrians [35], as well as single-vehicle accidents [36,37]. It has been demonstrated that PC-crash has good performance in simulating single-vehicle (rollover) accidents [36][37][38][39][40].…”

Section: Methodsmentioning

confidence: 99%

Probability of Roadside Accidents for Curved Sections on Highways

Cheng

Pei

et al. 2020

Mathematical Problems in Engineering

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To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.

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Section: Methodsmentioning

confidence: 99%

Probability of Roadside Accidents for Curved Sections on Highways

Cheng

Pei

et al. 2020

Mathematical Problems in Engineering

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“…Due to their chaotic nature and sensitivity to small changes in initial conditions, the individual impacts that occur in rollover crash tests are generally not repeatable or predictable. Efforts have been made to model individual impacts in rollover crash tests using either impulsemomentum equations [13] or computational modeling packages [12,14]. However, the usefulness of complicated models in reconstructing real world crashes is limited by their fidelity and by the amount of information typically available to the reconstructionist.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Model of Rolling and Sliding in Rollover Crashes

Funk¹,

Wirth²,

Bonugli³

et al. 2012

SAE Technical Paper Series

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Rollover crashes are often difficult to reconstruct in detail because of their chaotic nature. Historically, vehicle speeds in rollover crashes have been calculated using a simple slide-tostop formula with empirically derived drag factors. Roll rates are typically calculated in an average sense over the entire rollover or a segment of it in which vehicle roll angles are known at various positions. A unified model to describe the translational and rotational vehicle dynamics throughout the rollover sequence is lacking. We propose a pseudocylindrical model of a rolling vehicle in which the rotational and translational dynamics are coupled to each other based on the average frictional forces developed during ground contacts. We describe the model as pseudo-cylindrical because vertical motion is ignored but the ground reaction force is not constrained to act directly underneath the center of gravity of the vehicle. The tumbling phase of a rollover is modeled in three distinct phases: an initial brief airborne phase between roll initiation and the first ground contact, an early phase in which relative sliding between the perimeter of the vehicle and the ground causes the roll rate to increase, and a later phase in which the vehicle rolls without sliding and the roll rate decreases. In the early phase, the average vehicle deceleration is higher and is governed by sliding friction. In the later phase, the average vehicle deceleration is lower and is governed by geometric factors. Model predictions were fit to data from 12 well-documented rollover crashes in order to derive empirical values for the model parameters. In 11 out of the 12 rollovers studied, the model predictions matched the actual results with good accuracy. The results validate the underlying physical principles of the model and provide data that can be used to apply the model to real world rollovers. The proposed model provides a physical basis for understanding vehicle dynamics in rollovers and may be used in certain cases to improve the accuracy of a rollover reconstruction.

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