Detection of crack in structure using dynamic analysis and artificial neural network

Maurya, Manisha; Sadarang, Jatin; Panigrahi, Isham

doi:10.5267/j.esm.2019.11.002

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…and assembled in equations ( 7) and (8). Initially, the employed iterative procedure considers the convergent contact status of the previous time t .…”

Section: Incremental-iterative Proceduresmentioning

confidence: 99%

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Prediction of crack depth and position in vibrating beams using artificial neural networks

Bouboulas¹,

Nikolakopoulos²,

Anifantis³

2022

Diagnostyka

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The aim of this paper is to develop a finite element procedure for crack prediction in vibrating beams. Based on this procedure, full frictional contact conditions are introduced between the crack surfaces in order to consider the breathing of crack. The region surrounding the crack is simulated by two-dimensional finite elements. An incremental-iterative procedure is employed to solve the nonlinear dynamic equations governing this problem. The obtained time response is processed with Fast Fourier Transform to extract its frequency components. The first three natural frequencies are input to a trained Artificial Neural Network for depth and position prediction of the crack. This study is validated for a dynamic loading cantilever beam. It is found that the proposed procedure is capable of predicting the crack depth and position with high accuracy.

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“…and assembled in equations ( 7) and (8). Initially, the employed iterative procedure considers the convergent contact status of the previous time t .…”

Section: Incremental-iterative Proceduresmentioning

confidence: 99%

“…In 1992, Wu and co-workers [3] reported for first time the successful application of ANN in crack detection utilizing modal parameters. Since then, many researchers have concluded the potential of ANN for crack detection in beam-like structures [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of crack depth and position in vibrating beams using artificial neural networks

Bouboulas¹,

Nikolakopoulos²,

Anifantis³

2022

Diagnostyka

View full text Add to dashboard Cite

show abstract

“…The most popular ML models for crack detection are support vector machines (SVMs) [14,15], artificial neural networks (ANNs) [16,17] and random forests (RFs) [18,19]. In ML methods, crack detection is usually accompanied by crack type classification, including the preprocessing step of feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning

et al. 2022

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Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segmentation models with red–green–blue (RGB) marble slab images using five-fold cross-validation, providing consistent evaluation metrics in terms of Intersection over Union (IoU), precision, recall and F1 score to identify the segmentation challenges related to marble cracks’ physiology. Comparative results reveal the FPN model as the most efficient architecture, scoring 71.35% mean IoU, and SE-ResNet as the most effective feature extraction network family. The results indicate the importance of selecting the appropriate Loss function and backbone network, underline the challenges related to the marble crack segmentation problem, and pose an important step towards the robotic automation of crack segmentation and simultaneous resin application to heal cracks in marble-processing plants.

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“…For this reason, several authors have used this technology to solve various challenges. Combining finite element analysis and artificial neural networks (ANN), Maurya et al [45] carried out an analysis to investigate the influence of crack location on the first three natural frequencies of a cantilever beam; then, they designed and trained an ANN. The errors in the results obtained by the two methods were not considerable.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Circular Arc Crack Depths and Locations in Rotary Drilling Pipes Subjected to Free Vibrations

et al. 2022

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Late detection of cracks can lead to serious failures and damages of drilling components, especially drill pipes and drill bits. Currently, the widely used method of repairing rotary drilling systems after a failure is corrective maintenance. Although this strategy has shown its effectiveness in many cases, waiting for a failure to occur and then performing a repair can be an expensive and time-consuming operation. Thus, the use of preventive maintenance under the aspect of periodic inspections can solve this problem and help engineers detect cracks before they reach critical sizes. In this study, modal analysis and finite element analysis (FEA) combined with artificial neural networks (ANN) were used to dynamically estimate the depth and location of a circular arc crack in the drill pipes of rotary drilling systems. To achieve this goal, a detailed analytical approach based on Euler–Bernoulli beam theory was adopted to validate the first four natural frequencies found by FEA for an undamaged pipe. Afterwards, an arc crack was assigned to the pipe already created using Abaqus, and the first four natural frequencies were obtained for each depth and location of the crack. Simulations with FEA led to the generation of a dataset with two inputs—depth and location of cracks—and four outputs: natural frequencies. Moreover, a multilayer perceptron (MLP) was designed and trained by the data collected from simulations. Finally, a comparison between the results obtained by FEA and ANN was performed, where both approaches showed a good agreement in predicting the depth and location of cracks.

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Detection of crack in structure using dynamic analysis and artificial neural network

Cited by 7 publications

References 31 publications

Prediction of crack depth and position in vibrating beams using artificial neural networks

Prediction of crack depth and position in vibrating beams using artificial neural networks

Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning

Estimation of Circular Arc Crack Depths and Locations in Rotary Drilling Pipes Subjected to Free Vibrations

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