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

• Main Authors: Eugene OBrien, Muhammad Arslan Khan, Daniel Patrick McCrum, Aleš Žnidarič
• Format: Article
• Language: English
• Published: MDPI AG 2020-01-01
• Series: Applied Sciences
• Subjects: bridge health monitoring; bwim; structure dynamics; damage detection; vehicle-bridge interaction; shm
• Online Access: https://www.mdpi.com/2076-3417/10/2/663

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.