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B. Miller and L. Ziemianski

Department of Structural Mechanics, Rzeszow University of Technology, Rzeszow, Poland

doi:10.4203/ccp.64.1.5

B. Miller, L. Ziemianski, "Neural Networks in Updating of a Twelve Storey Frame Model", in B.H.V. Topping, (Editor), "Computational Engineering using Metaphors from Nature", Civil-Comp Press, Edinburgh, UK, pp 31-36, 2000. doi:10.4203/ccp.64.1.5

Abstract

This paper presents an application of Artificial Neural Networks (ANN) for updating of a mathematical model of a structure. Neural networks, which predict the value of stiffnesses on the basis of Frequency Response Function (FRF) have been built. Wide variety of network architectures and different input definitions have been tested. In order to reduce the dimension of input vector necessary for the model updating the FRF is either compressed even to one tenth of initial number of values by a dedicated neural network called Characteristic Replicator or geometrical characteristics of selected FRF bands are calculated and used as input data. The sensitivity of this method in predicting of selected model parameters has been checked.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 64 COMPUTATIONAL ENGINEERING USING METAPHORS FROM NATURE Edited by: B.H.V. Topping Paper I.5 Neural Networks in Updating of a Twelve Storey Frame Model B. Miller and L. Ziemianski Department of Structural Mechanics, Rzeszow University of Technology, Rzeszow, Poland doi:10.4203/ccp.64.1.5 purchase the full-text of this paper Full Bibliographic Reference for this paper B. Miller, L. Ziemianski, "Neural Networks in Updating of a Twelve Storey Frame Model", in B.H.V. Topping, (Editor), "Computational Engineering using Metaphors from Nature", Civil-Comp Press, Edinburgh, UK, pp 31-36, 2000. doi:10.4203/ccp.64.1.5 Abstract This paper presents an application of Artificial Neural Networks (ANN) for updating of a mathematical model of a structure. Neural networks, which predict the value of stiffnesses on the basis of Frequency Response Function (FRF) have been built. Wide variety of network architectures and different input definitions have been tested. In order to reduce the dimension of input vector necessary for the model updating the FRF is either compressed even to one tenth of initial number of values by a dedicated neural network called Characteristic Replicator or geometrical characteristics of selected FRF bands are calculated and used as input data. The sensitivity of this method in predicting of selected model parameters has been checked. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £52 +P&P)
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