Service Performance Indicators of Bridge Support Reaction Monitoring Big Data based on Statistically Stable Traffic Flow Load Effect Analysis Model

Abstract

Monitoring information on bridge structures in service can be used to identify structural damage or to assess the service performance of the structure in general. However, it is a challenging but significant task to use the monitoring information of the structural static effects under the natural traffic conditions during the operation period to identify the structural damage or overall performance. The challenge is to define and identify indicators that characterize structural damage and service performance through monitoring information on static effects under unknown traffic loads. To this end, this paper firstly establishes an analytical model for the support reaction of continuous girder bridges under the action of arbitrary traffic stream loading, and based on this a series of new indicators that can reflect the big data mechanical behavior laws of bridges in the statistically steady state are developed, i.e., the time-average state resultant force of the support reaction and the statistically steady state traffic stream equivalent uniform line load, as well as the normalized indicator of the time-average state support reaction resultant force obtained by extension, with a view to evaluating the service performance of the bridge structure. Through the established analytical model of bridge support reaction, it is theoretically proved that this type of indicator has excellent monitorable performance under statistically steady state traffic loading, which is not only easy to monitor and perceive, but also is not affected by the specific value of immediate traffic loading, and is only related to the static properties of the structure. It can be used for service performance monitoring and damage detection of bridge structures under service conditions, which is the good monitoring indicators in the background of big data.