NDLI: Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks

Content Provider	Springer Nature Link
Author	Aydin, Kamil Kisi, Ozgur
Copyright Year	2012
Abstract	This study investigates the efficiency of artificial neural networks (ANNs) in health monitoring of pristine and damaged beam-like structures. Beam modeling is based on Timoshenko theory. Two commonly used network models, multilayer perceptron (MLP) and radial basis neural network (RBNN), are used. Beam material and geometrical properties, beam end conditions and dynamically obtained data are used as input to the neural networks. The combinations of these parameters yield umpteenth input data. Therefore, to examine the effectiveness of ANNs, the frequency of intact beams is first tried to be determined by the network models, given the material and geometrical characteristics of beam elements and support conditions. The methodology to compute the vibrational data utilized in training the networks is provided. Showing the robustness of network models, the second stage of the study is carried out. At this stage, the crack parameters (e.g. the location and severity of crack) are estimated by the ANNs using the beam properties, beam end conditions and vibrational data, which consist of natural frequencies and mode shape rotation values. Despite the multiplexed input data, no data reduction schemes or multistage computations are executed in training and validation of neural network models. As a result of analysis runs, the optimal MLP and RBNN models are determined. Comparison of these models shows that the optimal RBNN algorithm performs better. The effectiveness of optimal ANN models in the presence of noise is also presented. As a conclusion, the trained network can be used as a diagnosis method in structural health monitoring of beam-like structures.
Starting Page	583
Ending Page	597
Page Count	15
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	24
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-11-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks Multilayer perceptron Radial basis neural network Timoshenko beam Crack Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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