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.

Meeting Name

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)


Document Type

Article - Conference proceedings

Document Version


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© 2007 SPIE, All rights reserved.

Publication Date

01 Mar 2007