• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3A
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• pp.199-206
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• 2011

In Korea, the current design codes for the bridge vessel collision load are based on AASHTO LRFD code which derived from the mean collision forces of the Woisin's test. To estimate the conservativeness of the code, in this study, the mean forces of head on collisions were evaluated from the mass-acceleration relationship of vessel and the deformation-kinetic energy relationship of bow those obtained from the series of nonlinear finite element analysis, and the mean forces were compared to that in AASHTO design code. As results, the variations of the mean forces versus the sizes of vessels were represented similar tendency, even those of the code are very conservative. However, the variations of mean collision force versus those of collision speeds were dominated by the plastic deformation of bow and it was differ from those of the code that have linear relationship with the collision speeds.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.10
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• pp.1053-1061
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• 2015

This paper discusses the analysis of the curl deformation behavior when a dynamic force is applied to a tearing tube installed on a flat die to predict the energy absorption capacity and deformation behavior. The deformation of the tips of the curling strips was obtained when the curl tips and tube body are in contact with each other, and a formula describing the energy dissipation rate caused by the deformation of the curl tips is proposed. To improve this formula, we focused on the variation of the curl radius and the reduced thickness of the tube. A formula describing the mean curl radius is proposed and verified using the curl radius measurement data of collision test specimens. These improved formulas are added to the theoretical model previously proposed by Huang et al. and verified from the collision test results of a tearing tube.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.104-113
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• 2013

New USNCAP has been carried out by NHTSA including front and side crash from MY2011. In this paper, test results for USNCAP Side crash were reviewed by statistical analysis. This review focused on side crash test results to investigate the effect of changes from new USNCAP side crash test protocol among 30 passenger cars. These results were summarized as followings. Total number of 5 star vehicles on the front seat dummy (16 vehicles, 53.3%) was slightly smaller than the rear seat's (17 vehicles, 56.7%) in MDB test. For the ES-2re dummy, chest injury, ie maximum rib deflection contributed to 66% in the mean value of $P_{joint}$. Pelvis injury was highly dependent upon performance up to 87% in the SID-IIs dummy cited on the rear seat in average $P_{joint}$. For Pole test, pelvis injury made contribution to the average performance to 83%. For standard deviation, it showed the largest value in the same body region as the mean value for each dummy. Overall front seat performance showed 14 vehicles, 44.6% with 5 star vehicles less than each MDB or Pole test result. This result showed that performances in MDB test were different pattern to Pole test on driver position. Number of 5star vehicles for overall side NCAP performance are 18 passenger cars (60%). Curtain airbag and driver thorax airbag were equipped in all test vehicles. One vehicle is equipped with thorax airbag in the rear seat. Results from two side tests considered as reliability problem, ie the cause for large standard deviation in side crash test. Consequently, the countermeasure for new USNCAP side crash test is essential to design the effective side structures for side collision and to control well dummy kinematics with curtain and thorax airbag in order to reduce chest and pelvis injuries.