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Paper Information

Journal:   MODARES TECHNICAL AND ENGINEERING   FALL 2008 , Volume - , Number 33 (SPECIAL ISSUE ON CIVIL ENGINEERING); Page(s) 83 To 93.
 
Paper: 

FINITE ELEMENT MODEL UPDATING OF A RAILWAY BRIDGE USING ARTIFICIAL NEURAL NETWORKS

 
 
Author(s):  ATAEI SH., AGHA KOUCHAK A.A.*, MAREFAT M.S., MOHAMADZADEH S.
 
* TARBIAT MODARES UNIVERSITY, TEHRAN, IRAN
 
Abstract: 
Field testing of bridge vibrations induced by passage of vehicles is an economic and practical form of bridge load testing. Data processing of this type of tests is usually carried out in a system identification framework using output measurements techniques which are categorized as parametric or nonparametric methods. These methods are based on the theory of probability. Learning theory which stems from two separate disciplines of statistical learning theory and neural networks, presents an efficient and robust framework for data processing for such tests. In this article, ADALINE- Adaptive Linear Neuron networks- with LMS (Least Mean Squares) learning rule have been adapted for strain and displacement sensors fusion of a railway bridge load test. The trained NN has been used for structural analysis and finite element (FE) model updating.
 
Keyword(s): LEARNING THEORY, NEURAL NETWORKS, SENSOR FUSION, RAILWAY BRIDGE, LOAD TEST, MODEL UPDATING
 
References: 
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