Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain

doi:10.1260/1369-4332.15.10.1689

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Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain

Journal Article•DOI•

Structural Damage Identification Using Improved RBF Neural Networks in Frequency Domain

Rajendra Machavaram¹, K. Shankar¹•Institutions (1)

Indian Institute of Technology Madras¹

01 Oct 2012-Advances in Structural Engineering (SAGE Publications)-Vol. 15, Iss: 10, pp 1689-1703

TL;DR: 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.

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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...

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Journal Article•DOI•

Machine learning for structural engineering: A state-of-the-art review

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Huu-Tai Thai

01 Apr 2022-Structures

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.

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89 citations

Journal Article•DOI•

Vibration suppression of printed circuit boards using an external particle damper

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P. Veeramuthuvel¹, K.K. Sairajan¹, K. Shankar²•Institutions (2)

Indian Space Research Organisation¹, Indian Institute of Technology Madras²

31 Mar 2016-Journal of Sound and Vibration

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.

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28 citations

Journal Article•DOI•

Improved Complex-valued Radial Basis Function (ICRBF) neural networks on multiple crack identification

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M. Rajendra¹, K. Shankar¹•Institutions (1)

Indian Institute of Technology Madras¹

01 Mar 2015

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.

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Abstract: Absolute mean percentage error (AMPE) of single crack and multiple crack identification using different RBF networks. A two-stage ICRBF neural network is developed for multiple crack identification.Conventional CRBF neural network is used in the first stage of ICRBF.Reduced search space moving technique is used in the second stage of ICRBF.Crack location and depth are identified using frequency domain vibration signals.ICRBF is more efficient followed by IRBF, CRBF, RBF and MLP neural networks. This paper introduces a new class of neural networks in complex space called Complex-valued Radial Basis Function (CRBF) neural networks and also an improved version of CRBF called Improved Complex-valued Radial Basis Function (ICRBF) neural networks. They are used for multiple crack identification in a cantilever beam in the frequency domain. The novelty of the paper is that, these complex-valued neural networks are first applied on inverse problems (damage identification) which come under the category of function approximation. The conventional CRBF network was used in the first stage of ICRBF network and in the second stage a reduced search space moving technique was employed for accurate crack identification. The effectiveness of proposed ICRBF neural network was studied first on a single crack identification problem and then applied to a more challenging problem of multiple crack identification in a cantilever beam with zero noise as well as 5% noise polluted signals. 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. It appears that IRBF neural network is a good compromise considering all factors like accuracy, simplicity and computational effort.

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14 citations

Journal Article•DOI•

Application of RBF neural network in prediction of particle damping parameters from experimental data

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P. Veeramuthuvel¹, P. Veeramuthuvel², K. Shankar¹, K.K. Sairajan²•Institutions (2)

Indian Institute of Technology Madras¹, Indian Space Research Organisation²

01 Apr 2017-Journal of Vibration and Control

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.

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Abstract: Particle damping is one of the recent passive damping methods and its relevance in space structural applications is increasing. This paper presents the novel application of a radial basis function (RBF) neural network to accurately 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. The prediction of particle damping using the RBF neural network is studied in comparison with the back propagation neural (BPN) network on an aluminum alloy beam structure with extensive experimental tests. The prediction accuracy of the RBF neural network is significant with 9.83% error compared to 12.22% obtained by the BPN network for a best case. Limited experiments were also carried out on a mild steel beam to study and compare the trends predicted in earlier studies. The relationships obtained by the proposed method readily provide useful guidelines in the design of particle dam...

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12 citations

Cites background or methods from "Structural Damage Identification Us..."

...The Euclidean distance is calculated from xm ci k k(2) (Machavaram and Shankar, 2012)....
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...where Wni is the weight vector in the output layer for the nth output node and i is the radial basis function of the ith node (Machavaram and Shankar, 2012)....
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...The output from the i th Gaussian neuron from an input xm is calculated using i ¼ exp xm cik k2 2i ð1Þ The Euclidean distance is calculated from xm cik k2 (Machavaram and Shankar, 2012)....
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...The output of the hidden neuron is computed as: yn¼ X i Wni i ð2Þ where Wni is the weight vector in the output layer for the nth output node and i is the radial basis function of the ith node (Machavaram and Shankar, 2012)....
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Journal Article•DOI•

Experimental and numerical studies on a test method for damage diagnosis of stay cables

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Yonghui An¹, Zhong Yue¹, Yanbin Tan¹, Jinping Ou¹•Institutions (1)

Dalian University of Technology¹

01 Feb 2017-Advances in Structural Engineering

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.

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Abstract: To diagnose the state of stay cables, a vibration-based model-free damage diagnosis method of stay cables using the changes in natural frequencies is further proposed and validated. The structural ...

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10 citations

Cites background from "Structural Damage Identification Us..."

...Changes in the physical properties will cause changes in the modal properties (Machavaram and Shankar, 2012)....
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References

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Journal Article•DOI•

Use of neural networks in detection of structural damage

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X. Wu¹, Jamshid Ghaboussi¹, James H. Garrett²•Institutions (2)

University of Illinois at Urbana–Champaign¹, Carnegie Mellon University²

17 Feb 1992-Computers & Structures

TL;DR: In this article, the authors explored the use of the self-organization and learning capabilities of neural networks in structural damage assessment, and trained a neural network to recognize the behavior of the undamaged structure as well as the behaviour of the structure with various possible damage states when subjected to the measurements of the structural response, it should be able to detect any existing damage.

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515 citations

"Structural Damage Identification Us..." refers background or methods in this paper

...In recent years, however Artificial neural networks (ANNs) have been widely accepted as an alternative way to estimate and predict the extent and location of damage in complex structures (Wu et al. 1992; Elkordy et al. 1993)....
[...]
...Wu et al. (1992) used BPN network to simulate the damage of a three-storey frame structure excited by base earthquake acceleration....
[...]

Journal Article•DOI•

Identification of Nonlinear Structural Dynamic Systems

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Chung Bang Yun¹, Masanobu Shinozuka¹•Institutions (1)

Columbia University¹

01 Jan 1980

TL;DR: In this article, two methods of system identification of n degrees-of-freedom structural dynamic systems are studied and applied to identification of the hydrodynamic coefficient matrices associated with nonlinear drag and linear inertia forces appearing in the equations of motion of offshore structures subjected to wave forces.

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Abstract: Two methods of system identification of n degrees-of-freedom nonlinear structural dynamic systems are studied and applied to identification of the hydrodynamic coefficient matrices associated with nonlinear drag and linear inertia forces appearing in the equations of motion of offshore structures subjected to wave forces. These two methods, being essentially the methods of state estimation, use nonlinear Kalman filtering algorithms which can be applied to parameter estimation problems by regarding each of the parameters involved in the system as an augmented state variable. One of these methods uses the extended Kalman filter while the other was the iterated linear filter-smoother. Analytical simulation studies are performed for two degrees-of-freedom structural systems on the basis of artificially generated input and output observations under the various output noise conditions. Both methods yield good estimates even under the conditions of fairly large amounts of output noise and are moderately...

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277 citations

Journal Article•DOI•

Substructural identification using neural networks

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Chung Bang Yun¹, Eun Young Bahng¹•Institutions (1)

KAIST¹

01 Jun 2000-Computers & Structures

TL;DR: This study presents a method for estimating the stiffness parameters of a complex structural system by using a backpropagation neural network to overcome the issues associated with many unknown parameters in a large structural system.

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216 citations

"Structural Damage Identification Us..." refers methods in this paper

...Yun and Bahng (2000) employed a back propagation neural network to predict the structural element level stiffness values using the natural frequencies and mode shapes as input to the network....
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...In the first stage, the conventional RBF network is trained with the effective input-output patterns selected by the Latin hypercube sampling (LHS) technique (Yun and Bahng 2000) and it is used to predict the damage of the test case....
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Journal Article•DOI•

A neural dynamics model for structural optimization—Theory

[...]

Hojjat Adeli¹, Hyo Seon Park¹•Institutions (1)

Ohio State University¹

03 Nov 1995-Computers & Structures

TL;DR: In this paper, a neural dynamics model is presented for optimal design of structures, which consists of two distinct layers: a variable layer and a constraint layer, where the number of nodes in the variable and constraint layers correspond to the numbers of design variables and constraints in the structural optimization problem.

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156 citations

"Structural Damage Identification Us..." refers background in this paper

...Though the BPN networks possess excellent pattern recognition, interpolation and generalization abilities; these face difficulties such as entrapment into local minima and slow rate of learning (Adeli and Park 1995)....
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Journal Article•DOI•

Neural networks trained by analytically simulated damage states

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M. F. Elkordy, Kuo-Chun Chang, G. C. Lee

01 Apr 1993-Journal of Computing in Civil Engineering

TL;DR: Identifying changes in the vibrational signatures of a structure is a promising tool in structural monitoring and a neural network can be used for this purpose.

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Abstract: Identifying changes in the vibrational signatures of a structure is a promising tool in structural monitoring. Neural networks can be used for this purpose. For a neural network to diagnose damage ...

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154 citations

"Structural Damage Identification Us..." refers background or methods in this paper

...In recent years, however Artificial neural networks (ANNs) have been widely accepted as an alternative way to estimate and predict the extent and location of damage in complex structures (Wu et al. 1992; Elkordy et al. 1993)....
[...]
...Elkordy et al. (1993) modelled the damage states of a five-storey frame by adopting three BPN networks....
[...]