Using Full-Scale Observations on Footbridges to Estimate the Parameters Governing Human–Structure Interaction

Abstract

The further development and improvement of prediction models for crowd-induced vibrations of footbridges requires detailed information on representative operational loading data. In this paper, an inverse method is used to estimate the parameters that govern human–structure interaction from the resulting structural response. The parameters of interest concern the dynamic characteristics of a mass-spring-damper (MSD) system, applied to describe the mechanical interaction between the pedestrian and the structure. The dynamic characteristics of the MSD interaction model are estimated by minimizing the discrepancy between the observed and the simulated power spectral density of the structural response. The parameter estimation procedure assumes that the dynamic behavior of the empty structure, the average weight, and the distribution of step frequencies in the crowd are known. The proposed approach is verified using numerical simulations and the influence of modeling errors is investigated. The results show that as footbridges and the human body are by nature lightly (≲2%) and strongly (≈30%) damped, respectively, the structural response is most sensitive to small variations in the natural frequency of the MSD interaction model. The results, furthermore, show that the parameter estimation problem is mostly sensitive to errors related to the mean value of the distribution of step frequencies and the structural modes’ natural frequency and modal mass. The impact of the structural modeling errors is found to decrease as the impact of human–structure interaction increases. Next, the approach is applied to two real footbridges where the walking behavior and the structural response induced by high pedestrian densities are observed. The results show that an estimate of the natural frequency (≈3.0 Hz) and damping ratio (≈34%) of the MSD interaction model is obtained that is in accordance with recent findings in the literature. These estimates are, however, for the first time ever, it is believed, based on full-scale observations involving high pedestrian densities.