Application of semi-Bayesian neural networks in the identification of load causing beam yielding

Authors:
Bartosz Miller
Affiliations:
Rzeszow University of Technology, Rzeszow, Poland
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

Citing 4
Cited 0

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Identification of load parameters for an elastic-plastic beam basing on dynamic characteristics changes

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Modal analysis and modified cascade neural networks in identification of geometrical parameters of circular arches

Computers and Structures

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Abstract

Possible yielding of the cross-section of a structure might significantly decrease the safety margin of the investigated structure. The cross-section yielding causes a change of structure stiffness and, further, dynamic characteristics. The measurement of the changes of the dynamic parameters may provide information necessary to identify the load causing yielding of the cross-section, and further the yielding index (calculated when the load causing yielding is known) enables evaluation of structure safety margin. In the paper the semi-Bayesian neural networks are utilized to solve the identification problem.