Real-Time Condition Assessment of the Bill Emerson Cable-Stayed Bridge using Artificial Neural Networks
The Bill Emerson Cable-stayed Bridge is a newly built 1206 meter long structure crossing the Mississippi River. Due to its criticality and proximity to the New Madrid Seismic Zone, a seismic monitoring system consisting of 84 accelerometers was established for the bridge and its adjacent area. This paper is focused on a three-step artificial neural network strategy that was developed to identify the stiff of the bridge structure using the field measured dynamic response time histories without performing any eigenvalue analysis. The first step is to develop and train an emulator neural network for accurate prediction of the responses of the Bill Emerson Cable-stayed Bridge model, which represents the healthy state of the structure. A finite element model of the cable-stayed bridge was established, which represents the as-built bridge and was calibrated with the measured earthquake data from the seismic monitoring system. The second step is to establish and train a parameter evaluator neural network for relating the stiff reduction in the model bridge to the response prediction error by the emulator neural network. The third and last step is to identify the location and degree of stiff reduction in the Bill Emerson Cable-stayed Bridge.
W. Wang et al., "Real-Time Condition Assessment of the Bill Emerson Cable-Stayed Bridge using Artificial Neural Networks," Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems (2007, San Diego, CA), vol. 6529 PART 1, SPIE, Mar 2007.
The definitive version is available at https://doi.org/10.1117/12.715244
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems (2007: Mar. 19-22, San Diego, CA)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Artificial Neural Network; Bill Emerson Cable-Stayed Bridge; Condition Assessment; Real-Time Condition Assessment; Seismic Monitoring System; Stiffness Identification; Accelerometers; Condition Monitoring; Earthquake Effects; Eigenvalues And Eigenfunctions; Real Time Systems; Seismic Response; Neural Networks
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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