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An Expectation-maximization Algorithm-based Framework for Vehicle-vibration-based Indirect Structural Health Monitoring of Bridges

JINGXIAO LIU, SUSU XU, MARIO BERGÉS, JACOBO BIELAK, JAMES H. GARRETT, HAE YOUNG NOH

Abstract


We propose a vehicle-vibration-based indirect structural health monitoring (SHM) framework that uses acceleration signals collected from within a moving vehicle to identify global modal and structural parameters of a full-scale and in-service bridge. Motivated by many benefits of indirect sensing methods, such as low-cost, low-maintenance and no interruption to traffic, researchers have in the past presented different algorithms and evaluated them on several simulation and lab-scale datasets. However, the uncertainties of the real-world vehicle-bridge interaction system and limited training data may cause previous methods to fail on full-scale bridges. To address these uncertainties, we 1) cast the vehicle-bridge interaction system as a linear time-varying Gaussian statespace model, which is not only able to estimate unobserved bridge responses but also able to add a stochastic process for modeling uncertainties, and 2) propose a hybrid algorithm that uses non-linear least squares and the expectation-maximization algorithm to estimate modal and structural parameters of the bridge using partially observed data (only the vehicle’s dynamic response is observed). We conducted field experiments on a steel truss bridge carrying two rail lines across the Monongahela River in Pittsburgh, Pennsylvania. For estimating the damage that is simulated by placing stationary trains on the bridge, our proposed approach has a 36.3% error reduction compared to a fully data-driven method. The results show that our proposed algorithm provides a potentially practical approach for continuous monitoring of in-service bridges.


DOI
10.12783/shm2019/32132

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