Footbridges, like all building structures, must be designed in a way that ensures their safe and comfortable use. Steel footbridges characterised by low vibration damping often turn out to be a structure susceptible to the dynamic influence of users during various forms of their activity. For these structures, the impact of running users may be a key type of dynamic load for the verification of the serviceability limit state due to vibrations. In the literature, there are several proposals for models of dynamic load generated by runners (models of ground reaction forces – GRF). The paper presents the characteristics, analyses and comparisons of selected GRF load models. The analyses were performed using the GRF recorded during the laboratory tests of runners (tests planned and carried out by the author) and the GRF determined using various load models. In order to illustrate the accuracy of the estimation of the dynamic response of the structure, depending on the GRF model used, dynamic field tests and dynamic numerical analyses of the selected steel footbridge were carried out. The obtained results were analysed and compared.

An iterative neural network framework is proposed in this paper for the human-induced Ground Reaction Forces (GRF) replication with an inertial electrodynamic mass actuator (APS 400). This is a first approach to the systematization of dynamic load tests on structures in a purely objective, repeatable and pedestrian-independent basis. Therefore, an inversion-free offline algorithm based on Machine Learning techniques has been applied for the first time on an electrodynamic shaker, without requiring its inverse model to tackle the inverse problem of successful force reconstruction. The proposed approach aims to obtain the optimal drive signal to minimize the error between the experimental shaker output and the reference force signal, measured with a pair of instrumented insoles (Loadsol©) for human bouncing at different fre- quencies and amplitudes. The optimal performance, stability and convergence of the system are verified through experimental tests, achieving excellent results in both time and frequency domain.