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Bayesian neural networks for bridge integrity assessment

Arangio, S. and Beck, J. L. (2012) Bayesian neural networks for bridge integrity assessment. Structural Control and Health Monitoring, 19 (1). pp. 3-21. ISSN 1545-2255. doi:10.1002/stc.420.

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In recent years, neural network models have been widely used in the Civil Engineering field. Interesting enhancements may be obtained by re-examining this model from the Bayesian probability logic viewpoint. Using this approach, it will be shown that the conventional regularized learning approach can be derived as a particular approximation of the Bayesian framework. Network training is only a first level where Bayesian inference can be applied to neural networks. It can also be utilized in another three levels in a hierarchical fashion: for the optimization of the regularization terms, for data-based model selection, and to evaluate the relative importance of different inputs. In this paper, after a historical overview of the probability logic approach and its application in the field of neural network models, the existing literature is revisited and reorganized according to the enunciated four levels. Then, this framework is applied to develop a two-step strategy for the assessment of the integrity of a long-suspension bridge under ambient vibrations. In the first step of the proposed strategy, the occurrence of damage is detected and the damaged portion of the bridge is identified. In the second step, the specific damaged element is recognized and the intensity of damage is evaluated. The Bayesian framework is applied in both steps and the improvements in the results are discussed.

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Additional Information:© 2010 John Wiley & Sons, Ltd. Received 22 May 2009; Revised 28 July 2010; Accepted 7 August 2010. Article first published online: 4 Nov 2010. Stefania Arangio acknowledge Prof. Fabio Casciati who brought the authors into contact by proposing the visiting period she spent at the California Institute of Technology.
Subject Keywords: Bayesian neural networks; probability logic approach; integrity assessment; long-span bridges; damage identification; model selection; automatic relevance determination
Issue or Number:1
Record Number:CaltechAUTHORS:20120229-072538440
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Official Citation:Arangio, S. and Beck, J. L. (2012), Bayesian neural networks for bridge integrity assessment. Structural Control and Health Monitoring, 19: 3–21. doi: 10.1002/stc.420
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:29512
Deposited By: Ruth Sustaita
Deposited On:29 Feb 2012 15:56
Last Modified:09 Nov 2021 17:07

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