Sensor fusion of a railway bridge load test using neural networks

• Published in: Expert systems with applications Vol. 29; no. 3; pp. 678 - 683
• Main Authors: Ataei, Sh, Aghakouchak, A.A., Marefat, M.S., Mohammadzadeh, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2005
• Subjects: Lad test; Learning theory; Model updating; Neural networks; Railway bridge; Sensor fusion; Lad test; Sensor fusion; Learning theory; Neural networks; Railway bridge; Model updating
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2005.04.038

Field testing of bridge vibrations induced by passage of vehicle is an economic and practical form of bridge load testing. Data processing of this type of tests are 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 its origin from two separate disciplines of statistical learning theory and neural networks, presents an efficient and robust framework for data processing of such tests. In this article, the linear two layer feed forward neural network (NN) with back propagation learning rule has 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.