Structural Stiffness Identification of Skewed Slab Bridges with Limited Information for Load Rating Purpose

Abstract

This paper presents a method for identifying structural stiffness of skewed reinforced concrete slab bridges with limited structural information using measured acceleration data. This information might be used for nondestructive evaluation, condition assessment, and load rating of bridges. A large number of slab bridges with different structural dimensions such as skew angle, span, width, and thickness was first analyzed using finite element method to obtain their first modal frequency. This population of data was then used to create an artificial neural network, which can predict a coefficient that plays an important role in identifying the flexural rigidity of slab bridges. This approach was applied to estimate the flexural rigidity of a highly skewed reinforced concrete slab bridge in the state of Virginia for load rating purpose. The bridge was instrumented with wireless accelerometers, and the vibration responses of the bridge under ambient loading and impact hammer test were recorded. An algorithm based on the variational mode decomposition was employed to identify modal properties of the bridge. Then, the flexural rigidity of bridge was computed from the established relationship between the first natural frequency and the flexural rigidity of bridge. Results show that the proposed method is capable of predicting structural stiffness, and can be used for load rating of bridges without structural information.