Detection of multiple complicated flaw clusters by dynamic variable-node XFEM with a three-step detection algorithm

Ma, Chunping; Yu, Tiantang; Lich, Le Van; Nguyen, Thanh-Tung; Bui, Tinh Quoc

doi:10.1016/j.euromechsol.2020.103980

Cited by 18 publications

(8 citation statements)

References 40 publications

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“…In References 20-29, to detect the internal flaws of a structure, an objective function was constructed based on its dynamic responses. These methods have been successfully applied to the quantitative detection of structural flaws, from the identification of a single flaw [8][9][10][11]17,21,29 to multiple flaws, [12][13][14][15][16]18,19,[22][23][24][25][26][27][28] and from flaw identification in homogeneous materials to that in heterogeneous ones. 20,23 These methods can identify void-and crack-like flaws.…”

Section: Introductionmentioning

confidence: 99%

“…For practical problems, the dynamic responses of a structure can be measured more easily. In References 20–29, to detect the internal flaws of a structure, an objective function was constructed based on its dynamic responses. These methods have been successfully applied to the quantitative detection of structural flaws, from the identification of a single flaw 8–11,17,21,29 to multiple flaws, 12–16,18,19,22–28 and from flaw identification in homogeneous materials to that in heterogeneous ones 20,23 .…”

Section: Introductionmentioning

confidence: 99%

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Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Jiang

Chen

Sun

et al. 2022

Numerical Meth Engineering

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The identification of internal structural flaws is an important research topic in structural health monitoring. At present, structural safety inspections based on nondestructive testing procedures mainly focus on qualitative analysis; hence, it is difficult to identify the scale of flaws quantitatively. In this article, an inversion model that can realize quantitative detection is proposed by combing the scaled boundary finite element method (SBFEM) with deep learning. First, the lamb wave propagation processes in thin structures containing flaws were simulated using the SBFEM, and the echo wave signal at an observation point was recorded and paired with the flaw information. Then, the paired SBFEM datasets were used to train the deep learning model employing a convolutional neural network. A flaw classification and identification model based on SBFEM datasets was established. For a thin structure with unknown flaw information, and by using the measured observation point signals, the established deep learning model can predict flaw information in real time. Finally, the model performance was verified using several numerical examples. The results show that the proposed model can accurately predict flaw information and is robust against noise; thus, it offers an advantage over existing nondestructive testing procedures and a development in the field of structural health monitoring.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Jiang

Chen

Sun

et al. 2022

Numerical Meth Engineering

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“…A very recent study from Du et al 26 proposed a novel strategy for detecting multiple flaws based on dynamic XFEM in the time domain and an improved artificial bee-colony algorithm. Ma et al 27 had developed dynamic variable-node XFEM, a method with a three-step algorithm for the detection of multiple complicated flaw clusters. Although dynamic variable-node XFEM 27,28 alleviated significantly the computational effort in forward analysis, the time requirements of dynamic analysis still greatly exceed those of static analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Ma et al 27 had developed dynamic variable-node XFEM, a method with a three-step algorithm for the detection of multiple complicated flaw clusters. Although dynamic variable-node XFEM 27,28 alleviated significantly the computational effort in forward analysis, the time requirements of dynamic analysis still greatly exceed those of static analysis. Structural dynamic characteristics (frequencies, mode shapes) and dynamic responses are closely related to structural physical parameters (e.g., mass, stiffness, and damping).…”

Section: Introductionmentioning

confidence: 99%

Combining dynamic XFEM with machine learning for detection of multiple flaws

Jiang

Zhao

2021

Numerical Meth Engineering

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A novel methodology for multiple flaw detection is presented in this study. It combines the dynamic extended finite element method (XFEM) with machine learning for the first time. The extreme learning machine (ELM) is chosen as a learning rule for modeling and prediction. The XFEM is employed to overcome the issues associated with the large quantity of input data required for ELM network training, whereas the ELM itself is used to bypass the time-consuming repeated analyses ordinarily required for the detection of multiple flaws. A large amount of potential flaw data for a structure is quasi-randomly generated by a Sobol sequence. For each effective flaw datum, the dynamic XFEM with circular/elliptical void enrichment is used to compute the structural dynamic characteristics (i.e., frequencies and displacement mode shapes), which is possible because re-meshing is not required for each flaw. The available data generated from the results of XFEM analyses are used for ELM network training. According to the measured dynamic characteristics, the trained ELM is then utilized to predict the size and location of flaws. The results show that the proposed novel methodology can identify accurately the location and size of circular or elliptical structural flaws. The method also is more efficient than previously proposed approaches, as it can avoid time-consuming iterative analyses, and it is robust against noise.

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“…Han et al [31] proposed a triangular extended stochastic finite element method (T-XSFEM) for simulation of random void problems with the aid of variable-node elements to couple/link different mesh-scales. More recently, the V-XFEM are extended for detection of multiple complicated flaw clusters in large structures [32]. Even much efforts in the fracture analyses, to the best of the authors' knowledge, the attention has not been paid to the dynamic fracture problems.…”

mentioning

confidence: 99%

Dynamic fracture analysis of the linearly uncoupled and coupled physical phenomena by the variable-node multiscale XFEM

Yin

Zhang

Liu

et al. 2021

Engineering Fracture Mechanics

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Detection of multiple complicated flaw clusters by dynamic variable-node XFEM with a three-step detection algorithm

Cited by 18 publications

References 40 publications

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Combining dynamic XFEM with machine learning for detection of multiple flaws

Dynamic fracture analysis of the linearly uncoupled and coupled physical phenomena by the variable-node multiscale XFEM

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