Vehicle crash modelling using recurrent neural networks

Authors:
T. Omar;A. Eskandarian;N. Bedewi
Affiliations:
FHWA/NHTSA National Crash Analysis Center George Washington University, Virginia Campus 20101 Academic Way, Ashburn, VA 20147, U.S.A.;FHWA/NHTSA National Crash Analysis Center George Washington University, Virginia Campus 20101 Academic Way, Ashburn, VA 20147, U.S.A.;FHWA/NHTSA National Crash Analysis Center George Washington University, Virginia Campus 20101 Academic Way, Ashburn, VA 20147, U.S.A.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1998

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Abstract

The initial velocity and structural characteristics of any vehicle are the main factors affecting the vehicle response in case of frontal impact. Finite Element (FE) simulations are essential tools for crashworthiness analysis, however, the FE models are getting bigger, which increases the simulation time and cost. In the current research, an advanced Artificial Neural Network (ANN) was used to store the nonlinear dynamic characteristics of the vehicle structure. Therefore, several impact scenarios can be analyzed quickly with much less computational cost by using the trained networks. The equation of motion of the dynamic system was used to define the inputs and outputs of the ANN. The system dynamics was included in the network performance and the recurrent back-propagation learning rule was adapted in training the network. The results of the numerical examples indicated that the recurrent ANN can accurately capture the frontal crash characteristics of the impacting structures, and predict the crash performance of the same structures for any other crash scenario within the training limits.