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Operational modal analysis, model updating and response prediction bridge under the 2014 Napa Earthquake

University of British Columbia

Bridges constitute a critical and important part of the infrastructure of many cities’ transportation network. They are expensive to build and maintain, and the consequences of a sudden failure are very severe. Therefore, bridges are expected to have a high degree of reliability, which means that they have to perform above a life safety criterion under earthquake excitations. In a continuous effort to improve design guidelines, it is imperative to understand the behavior of existing bridges that are subjected to severe shaking. For this reason, continuous monitoring of bridges has become essential: not only to help determine if a bridge has been damaged but also to understand their response to strong earthquake motions. The work reported here includes an in-depth analysis of the behavior of the Vallejo- Hwy 37 Napa River Bridge during the 2014 California, Napa earthquake (M 6.0). The bridge located in Vallejo California connects Sears Point Road and Mare Island to Vallejo. It was built in 1967. The bridge was instrumented with 12 accelerometers on the superstructure and 3 accelerometers at a free-field site. An analysis of the recorded data of the accelerometers on the superstructure was carried out to determine the maximum displacement at mid-span, and to get the fundamental frequencies of the bridge during the excitation. A finite element (FE) model was developed based on the as-built drawings and model updaitng was perform. Finally, the updated model was used with the recorded ground motion of the 2014 Napa Earthquake to perform a time history analysis. The results were compared to the recorded data of the sensors located on the bridge. The peak displacement at mid-span in the longitudinal and transverse directions of the FE had a good match to the recorded peak displacement. It can be concluded that the updated FE model can capture the peak displacement at the bridge mid-span. It also shows that having a strong motion network can help engineers to better understand the behavior of structures under earthquake loading, by looking at the recorded data and identifying peak values of acceleration, velocity and displacement.

Text

2016-09-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/59197

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/