Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

Eugene Obrien,Daniel Patrick Mccrum,Aleš Žnidarič,Muhammad Arslan Khan

doi:10.3390/app10020663

Abstract

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

Highlights

Bridge structures are amongst the most critical components of transport infrastructure
Level I damage detection methods identify the presence of damage in the structure, Level II methods identify the location of damage, Level III methods quantify the extent of damage and Level IV methods predict the remaining service life of the structure [6]
Measured internalmean temperature of Šentvid bridge that crossed theFigure bridge within the interval of 20 °C

Summary

Introduction

Bridge structures are amongst the most critical components of transport infrastructure. In the United States, 9.1% of the total number of bridges in 2016 were found to be structurally deficient and to require substantial investment and improvements to operate [4]. An effective method of bridge damage detection is important to detect damage quickly when it occurs. Many methods of bridge damage detection have been proposed for road authorities to maintain an acceptable level of bridge safety [5]. These methods are classified using levels of damage identification. Visual inspection is the most frequently used method, but it can be expensive, time-consuming and disruptive to traffic. Stochino et al used a combination of visual inspection and

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Conclusion