Francisco Badea

Even though beam-type elements are widely adopted in the industry due to their low computational cost and potential time savings when modeling, they present a significant shortcoming given by their own formulation, which makes them incapable of accounting for local joint topology, which has a notable influence on the behavior of these structures. In this scenario, solutions that can mitigate this drawback while still providing improved results with simple models are of special interest. Many research works have focused on joint-specific approaches, as reflected in the literature. This paper introduces a novel generally improved beam model. This model uniquely features 4 nodes, 12 elastic elements, and 1 beam, contrasting starkly with the conventional beam elements that consist of merely 2 nodes and 1 element. This innovative model enhances the adaptability of modeled structures at the joint level. Crucially, it necessitates a methodology for the precise estimation of the elastic elements at the joint level. This article explores the capabilities of artificial neural networks for predicting the stiffness values derived from the calculated displacements at specific points within a complete structure. This research provides a complete analysis of the proposed methodology showing the significant limitations encountered for ANN when predicting finite element methodology (FEM)-derived values. The results and findings obtained in the article serve as a valuable reference paving the way for future studies involving finite element models and artificial neural networks.

The fatigue design of bus structures is directly dependent on the loads that the vehicle will support throughout its lifespan. The determination of such operational loads, obtained in form of accelerations through vehicle sensing, is usually done with proprietary and expensive acquisition systems. In this paper, a low-cost acquisition system is presented and studied, which is designed, calibrated, and oriented for this purpose, based on an Arduino UNO and a low-cost accelerometer. The acquisitions are adapted to obtain the operational loads of several urban bus lines in the city of Madrid. The obtained data is later processed in order to characterize the acceleration cycles in these structures, which can be used as an input for the appropriate structural design. As a result, it is proved that the low-cost acquisition system is adequate and provides a simple and cheap way to characterize the acceleration of these vehicles during ordinary service.