Journal ArticleDOI
Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain
Rajendra Machavaram,K. Shankar +1 more
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TLDR
The novel improved RBF network is shown to be a good damage identification strategy for multiple member structures compared to conventional RBF and existing hybrid methods in terms of accuracy and computational effort.Abstract:
This paper presents a novel two stage improved Radial basis function (RBF) neural network for the damage identification of multimember structures in the frequency domain. The improvement of the proposed RBF network is carried out in two stages, viz. (i) first stage damage prediction by conventional RBF network trained with effective input-output patterns and (ii) in the second stage, minimization of the prediction error below the predefined error tolerance (3%) by training the network with patterns from reduced search space located after the first stage prediction. The network effective input patterns are fractional frequency change ratios (FFCs) and damage signature indices (DSIs), and the corresponding output patterns are stiffness values or damage severity of the structure at different damage levels. A Latin hypercube search (LHS) technique is used for finding the effective input-output patterns from the search space to improve the training efficiency. The numerical simulation of structural damage iden...read more
Citations
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Journal ArticleDOI
Machine learning for structural engineering: A state-of-the-art review
TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.
Journal ArticleDOI
Vibration suppression of printed circuit boards using an external particle damper
TL;DR: In this article, the use of particle damper capsule on a Printed Circuit Board (PCB) and the development of Radial Basis Function neural network to accurately predict the acceleration response is presented.
Journal ArticleDOI
Improved Complex-valued Radial Basis Function (ICRBF) neural networks on multiple crack identification
M. Rajendra,K. Shankar +1 more
TL;DR: The results proved that, the proposed ICRBF and real-valued Improved RBF (IRBF) neural networks have identified the single and multiple cracks with less than 1% absolute mean percentage error as compared to conventional CRBF and RBF neural networks, mainly because of their second stage reduced search space moving technique.
Journal ArticleDOI
Application of RBF neural network in prediction of particle damping parameters from experimental data
TL;DR: In this article, a radial basis function (RBF) neural network was used to predict the modal damping ratio of a particle damping system using system input parameters such as particle size, particle density, packing ratio, and their effect at different modes of vibration.
Journal ArticleDOI
Experimental and numerical studies on a test method for damage diagnosis of stay cables
TL;DR: In this article, a vibration-based model-free damage diagnosis method of stay cables using the changes in natural frequencies is further proposed and validated, and a structural model is used to diagnose the state of stay cable.
References
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Fast learning in networks of locally-tuned processing units
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ReportDOI
Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review
TL;DR: A review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response is presented in this article, where the authors categorize the methods according to required measured data and analysis technique.
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TL;DR: The text presents both classic and modern statistical designs for discrete-event simulation and provides relatively simple solutions for selecting problems to simulate, how to analyze the resulting data from simulation, and computationally challenging simulation problems.