Omar, T., Eskandarian, and Bedewi, N.E. (2000) “Artificial Neural Networks for Modeling Dynamics of Impacting Bodies and Vehicles”, I. Mech. E. Journal of Multi-body Dynamics, Part K, Vol. 214 No. K3. pp. 133-142.

Abstract: An approach is demonstrated and tested for modelling the nonlinear multi-body dynamics problem of two impacting bodies. An advanced recurrent artificial neural network (ANN) is used to model this highly nonlinear dynamic problem. The equations of motion of the dynamic system are used to explain the required inputs for the ANN. The developed networks are trained to predict the acceleration profiles of the impacting objects. Each object is represented by a corresponding ANN. The mutual effect between the two objects, during the impact, is also considered by training the two ANNs simultaneously. Two numerical examples are used to demonstrate the ability of the new developed approach. The first is the dynamic impact problem of two nonlinear lumped systems. The second is two different vehicles in frontal impacts. For each case, the problem is numerically solved for several impact scenarios and the results are used to prepare the data required for training and testing the developed ANNs. The results of the two numerical examples demonstrated the ability of the ANNs to store the nonlinear dynamic characteristics of the impacting objects after a successful supervised training. The trained networks are capable of modelling nonlinear multi-body dynamics and impact problems.