Investigation on the pattern for train-induced strains of the long-span steel truss railway bridge

Detecting abnormal changes in bridge behavior is essential for bridge management. Influence lines are a generally accepted method to describe the quasi-static bridge behavior. Researchers have proposed that the ratio between the influence lines associated structural strains for a pair of sensors can be used as an indicator of abnormal structural condition. However, these methods have been developed for highway bridges; direct application to railway bridges is challenging, because (i) measured responses corresponding to each axle load are coupled, and (ii) train-induced longitudinal forces and bearing restraint produce additional strains in structural members. This research seeks to resolve these challenges to enable application to long-span railway bridges with spherical bearings. First, the concept of influence lines is illustrated using the elastic beam theory, providing an understanding of the underlying mechanisms of the train-induced responses. Accordingly, two condition indicators base on the axial strain in a pair of structural members are given (i.e., the ratio between integral areas of pseudo-static strains and the ratio between average pseudo-static strain amplitude). In addition, the statistical patterns of the indicators are studied using numerical simulation. Subsequently, the strain patterns are illustrated for field monitoring data from a long-span steel truss railway bridge. Herein, the calculations for the condition indicators from strain measurements are presented, and the pre-processing of the indicators is conducted to consider bridge bearing properties. Results show that the condition indicators are naturally clustered according to tracks and the directions of two consecutive trains; each cluster can be represented using a normal distribution. Note that these distributions do not change with the variation of train loads (i.e., train weight and train speed). The recognized pattern provides a potential approach for the identification of abnormal behavior of railway bridges.