基于有限元法和神经网络技术的汽车碰撞事故再现

为充分利用事故变形信息,提出采用有限元法和神经网络技术进行事故再现的方法.在该方法中,首先采用数字测量技术得到事故车辆变形关键点的测量值,采用有限元仿真技术得到此关键点的计算值.将事故发生前的车辆运动参数作为神经网络的输入数据,关键点变形量测量值与仿真计算值的偏差作为神经网络的输出数据,将汽车碰撞仿真结果作为网络训练样本,对训练完成的神经网络进行优化求解得到事故发生瞬间的车辆运动参数.应用此方法对一起车-障碍物碰撞事故案例进行再现分析,建立整车、障碍物及地面有限元模型,选取前纵梁及挡泥板上的11个定位孔与螺栓孔作为变形量测量的关键点,再现分析结果验证了该方法的有效性,为事故责任鉴定提供了科学依据.

VEHICLE CRASH ACCIDENT RECONSTRUCTION BASED ON FEM AND NEURAL NETWORKS

To utilize the deformation of the vehicle and the collision objects in the accidents fully,a new vehicle crash ac- cident reconstruction method by means of the finite element method and neural networks techniques is presented.In the present method,the digital measurement technology is adopted to acquire the deformation of key points on the main en- ergy-absorbing parts as the indices to evaluate the accident. And the non-linear explicit finite element code is adopted to simulate the crash accidents in order to acquire the calculation values of these indices.On the basis of the numerical results of the crash accidents,a three-layer recurrent neural network is applied to generate an approximated function of the initial crash parameter and the deformation index.The finite element analy- ses are used to generate the examples for the training and test sets of the neural network.These results could be used to train the neural network by back-propagation learning rule.Applica- tion the present method to one vehicle-to-wall accident,at first, the finite element model of the auto-body,wall and the sur- rounding are finished,then the eleven key points on the frontal longitudinal beam and the mudguard are chosen,reconstruction result are solved by comparing the deformation of measuring in the real accident with the data of the simulation results.It is proved to be effective on analysis of this kind of accidents,and so can provide a scientific foundation for accident judgments.