Crack detection in beam-like structures

Rosales, Marta B.; Filipich, C.P.; Buezas, Fernando Salvador

doi:10.1016/j.engstruct.2009.04.007

Cited by 64 publications

(27 citation statements)

References 11 publications

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“…A cantilever beam was used and the capability of ANNs for verifying modal updating parameters for the numerical model using experimental modal data was shown. There were many other research efforts that attempted to apply ANNs to identify damage in structural engineering (Hakim and Abdul Razak, 2014, 2013Lu and Tu, 2004;Ni et al, 2006;Rosales et al, 2009).…”

Section: Introductionmentioning

confidence: 98%

Ensemble neural networks for structural damage identification using modal data

Hakim

Razak

Ravanfar

2015

International Journal of Damage Mechanics

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Damage in a structure is defined as changes to its geometric and material properties, leading to a reduction in the structural stiffness which negatively affects the performance of the structure. Reduction in the structural stiffness produces changes in the modal parameters such as the natural frequencies and mode shapes. Artificial neural networks (ANNs) have been applied extensively in recent years due to their excellent pattern recognition ability that is useful for structural damage identification purposes. In this paper, ANNs based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations were applied to generate dynamic parameters of the first five flexural modes of structures. In damage identification using ANNs, five individual networks corresponding to mode 1 to mode 5 were trained, and then a method based on neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble network has the advantages of all the individual networks from different vibrational modes. The results showed that ensemble neural networks have a strong potential for structural damage identification.

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Section: Introductionmentioning

confidence: 98%

Ensemble neural networks for structural damage identification using modal data

Hakim

Razak

Ravanfar

2015

International Journal of Damage Mechanics

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“…The analysis results showed that SVM is a little better in locating and sizing the cracks. The inverse problem of finding the crack location and depth in a cantilever Euler-Bernoulli beam and a rotating beam was studied by Rosales et al (2009). Two approaches, power series technique (PST) and ANNs, were presented to solve the inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Damage Diagnosis in Beam-Like Structures by Artificial Neural Networks

Aydin

Kisi

2015

Journal of Civil Engineering and Management

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Applicability of artificial neural networks is examined in determining the natural frequencies of intact beams and crack parameters of damaged beams. Multi-layer perceptron (MLP) and radial basis neural networks (RBNN) are utilized for training and validation of input data. In the first part of the study, the first four frequencies of free vibration are predicted based on beam properties by the networks. Showing the effectiveness of the neural networks in predicting the vibrational frequencies, the second part of the study is carried out. At this stage of the inverse problem, the frequencies and mode shape rotation deviations in addition to beam properties are used as input to the networks to determine the crack parameters. Different hidden nodes, epochs and spread values are tried to find the optimal neural networks that give the lowest error estimates. In both parts of the study, the RBNN model performs better. The robustness of the network models in the presence of noise is also shown. It is shown that the optimal MLP network predicts the crack parameters slightly better in the presence of noise. As a conclusion, the trained RBNN model can be used in health monitoring of beam-like structures as a crack identification algorithm.

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“…For example, Farrar and Worden (2012) focused on machine learning-based methods, such as decision trees, nearest neighbor classifiers, and neural networks, for fault detection in consideration of several general-purpose applications. Artificial neural networks (ANNs) were used to detect bearing defects of induction motors (Zarei et al 2014;Schmitt et al 2015) and were applied to detect cracks in beam-like structures (Rosales et al 2009). Crack detection was also investigated in Xiang et al (2013) using the wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Classification in Cantilever Beams Through Vibration Signal Analysis and Higher-Order Statistics

Barbosa

Ferreira

Pereira

et al. 2016

J Control Autom Electr Syst

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A method for detecting and classifying faults in an aluminum cantilever beam is proposed in this paper. The method uses features based on second-, third-and fourthorder statistics, which are extracted from the vibration signals generated by the cantilever beam. Fisher's discriminant ratio (FDR) is used for feature selection, and an artificial neural network is used for fault detection and classification. Three different degrees of faults (low, medium and high) were applied to the cantilever beam, and the proposed pattern recognition system was able to classify the faults, reaching performances ranging from 88 to 100 %. Moreover, the use of higher-order statistics-based features combined with FDR led to a compact feature space and provided satisfactory results.

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Crack detection in beam-like structures

Cited by 64 publications

References 11 publications

Ensemble neural networks for structural damage identification using modal data

Ensemble neural networks for structural damage identification using modal data

Damage Diagnosis in Beam-Like Structures by Artificial Neural Networks

Fault Detection and Classification in Cantilever Beams Through Vibration Signal Analysis and Higher-Order Statistics

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