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Accurate modeling of tire characteristics is one of the most challenging tasks. Many mathematical models can be used to fit measured data. Identification of the parameters of these models usually relies on least squares optimization techniques. Different researchers have shown that the proper selection of an initial set of parameters is key to obtain a successful fitting. Besides, the mathematical process to identify the right parameters is, in some cases, quite time-consuming and not adequate for fast computing. This paper investigates the possibility of using Artificial Neural Networks (ANN) to reliably identify tire model parameters. In this case, the Pacejka’s “Magic Formula” has been chosen for the identification due to its complex mathematical form which, in principle, could result in a more difficult learning than other formulations. The proposed methodology is based on the creation of a sufficiently large training dataset, without errors, by randomly choosing the MF parameters within a range compatible with reality. The results obtained in this paper suggest that the use of ANN to directly identify parameters in tire models for real test data is possible without the need of complicated cost functions, iterative fitting or initial iteration point definition. The errors in the identification are normally very low for every parameter and the fitting problem time is reduced to a few milliseconds for any new given data set, which makes this methodology very appropriate to be used in applications where the computing time needs to be reduced to a minimum.

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The finite element analysis of tubular structures is typically based on models constructed employing beam-type elements. This modeling technique provides a quick and computationally efficient option for calculation. Nevertheless, it shows a series of limitations related to the simplicity of this type of element, among which the inability of accounting for the stiffness behavior at the joint level is of notable importance when modeling complex tubular structures. Despite these limitations, the alternative of simulating complex tubular structures with shell- or volume-type elements is highly costly due to the complexity of the modeling process and the computational requirements. Previous research has proposed alternative beam models that improve the estimations when modeling these structures. These research validations were limited to simple models. This paper presents a validation process utilizing a previously developed beam T-junction model in a complex tubular structure, intended to be representative for buses’ and coaches’ upper structures. Results obtained reveal that the accuracy of beam element type models can be significantly improved with the adequate implementation of elastic elements to account for the real junction stiffness.

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