Performance of the generalized delta rule in structural damage detection

Barai, SV and Pandey, PC (1995) Performance of the generalized delta rule in structural damage detection. In: Engineering Applications of Artificial Intelligence, 8 (2). pp. 211-221.

PDF Performance_of_the_Generalized.pdf - Published Version Restricted to Registered users only Download (791kB) | Request a copy

Abstract

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

Item Type:	Journal Article
Publication:	Engineering Applications of Artificial Intelligence
Publisher:	Elsevier Science
Additional Information:	Copyright of this article belongs to Elsevier Science.
Keywords:	Artificial neural networks (ANN);backpropagation algorithm; bridge structure;damage detection;FEM;generalized delta rule (GDR);multilayer perceptrons;network architecture; structural identification;testing patterns;training patterns.
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	23 May 2011 10:07
Last Modified:	23 May 2011 10:07
URI:	http://eprints.iisc.ac.in/id/eprint/37849

Actions (login required)

View Item