Genetic Algorithm Supported by Influence Lines and a Neural Network for Bridge Health Monitoring
Publication: Journal of Structural Engineering
Volume 148, Issue 9
Abstract
This paper proposes a hybrid technique to solve the inverse problem of damage localization and severity estimation in beam structures. The first phase of the method involves the use of influence lines (ILs) to extract information about the damage location. Then, a genetic algorithm (GA), representing the core of the whole procedure, uses static parameters as displacements and rotations at a few points to evaluate the bending stiffness along the structure by updating a finite-element model. The information obtained in the first phase is used in the second phase for (1) reducing the number of design variables of the GA and the consequent computational time; and (2) improving the accuracy of GA solutions because it allows a suitably trained neural network to select proper values for the coefficients of the proposed cost function in the genetic algorithm. The procedure is applied to a test problem, a simply supported, prestressed concrete railway bridge located in northern Italy. Numerical experiments are also conducted to test the procedure when the beam length and geometric properties vary.
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Data Availability Statement
Some or all data, models or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Structural Engineering
Volume 148 • Issue 9 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 29, 2021
Accepted: Jan 26, 2022
Published online: Jun 28, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 28, 2022
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