Development and Parametric Evaluation of a Novel Load Distribution Model for Dynamic Assessments of Railway Bridges

Abstract

Increasing train speeds and axle loads result in greater demands on the reliability of predictions for vertical bridge accelerations in high-speed rail traffic. Complex multibody models, e.g., in the form of coupling beam models, allow for the explicit consideration of track–structure interaction, thereby improving prediction quality at the cost of significant computational and modelling effort. In this contribution, a computationally efficient alternative for the consideration of track-bridge interaction effects is developed that transfers the majority of the beneficial effects of the coupling beam to simple Euler–Bernoulli beam models without explicit coupling beam modelling. In this regard, the load-distributing effect of the ballast superstructure is extracted from train passage simulations of a track beam on a rigid base, whose coupling with the base via discrete spring and damper elements represents the elastic and dissipative properties of the ballast superstructure. The support force distributions determined under passing axle loads are assembled into train-specific load models and transferred to Euler–Bernoulli beam models. In addition, a probabilistic virtual bridge parameter field derived from real bridge data is developed to systematically evaluate the suitability of this novel load distribution model across a wide range of structural parameters. Compared to the load distribution model specified in standards, which specifies a distribution of axle loads into three individual loads, the load distribution model presented here shows significantly better agreement with the coupling beam model across all examined ballast superstructure and structural parameters, while consistently providing a conservative estimation of the structural response. The novel approach thus offers a physically sound method for efficiently accounting for the beneficial effects of the ballast superstructure in dynamic analyses.