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Use of genetic optimization in parameter identification of reinforced concrete bridge girders

Use of genetic optimization in parameter identification of reinforced concrete bridge girders Abstract The real response of a given structure, such as deflection, generally differs from prediction obtained from theoretical models. Hence, the need of periodic or long-term structural monitoring has become mandatory. The present paper describes an approach to localize the most suitable period to perform deflection monitoring in case of reinforced concrete bridges. The searched period allows making the best updates of theoretical models to better represent the structural behaviour with time. So, the genetic optimization is employed to update an existing law of flexural rigidity in order to minimize the difference between the measured and predicted deflections; measurements will be considered from different time periods. The proposed methodology is applied to a representative set of 21 RC T-beam bridges with variable parameters. Simulated measurements of static deflections based on weigh-in-motion data collected in some European countries are used in the application. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Innovative Infrastructure Solutions Springer Journals

Use of genetic optimization in parameter identification of reinforced concrete bridge girders

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Publisher
Springer Journals
Copyright
2020 Springer Nature Switzerland AG
ISSN
2364-4176
eISSN
2364-4184
DOI
10.1007/s41062-020-00339-2
Publisher site
See Article on Publisher Site

Abstract

Abstract The real response of a given structure, such as deflection, generally differs from prediction obtained from theoretical models. Hence, the need of periodic or long-term structural monitoring has become mandatory. The present paper describes an approach to localize the most suitable period to perform deflection monitoring in case of reinforced concrete bridges. The searched period allows making the best updates of theoretical models to better represent the structural behaviour with time. So, the genetic optimization is employed to update an existing law of flexural rigidity in order to minimize the difference between the measured and predicted deflections; measurements will be considered from different time periods. The proposed methodology is applied to a representative set of 21 RC T-beam bridges with variable parameters. Simulated measurements of static deflections based on weigh-in-motion data collected in some European countries are used in the application.

Journal

Innovative Infrastructure SolutionsSpringer Journals

Published: Dec 1, 2020

References