A data-driven approach based on wavelet analysis and deep learning for identification of multiple-cracked beam structures under moving load

Nguyen, Thi Hoa; Vuong-Cong, Luan; Le, Canh M.; Ngo, Nhi; Nguyen‐Xuan, H.

doi:10.1016/j.measurement.2020.107862

Cited by 38 publications

(16 citation statements)

References 54 publications

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“…The basis function expansions use a specific function system to expand the unknown load and then transform the MFI problem into the selection problem of the basis function coefficient, while the number of the basis function is regarded as the regularization parameter. Various types of basis functions have been proposed, including trigonometric functions [9], spline functions [10], wavelet functions [11] shape functions [12], etc. In the studies of intelligence algorithms, MFI is usually regarded as a mathematical optimization problem or a learningapplication process.…”

Section: Introductionmentioning

confidence: 99%

Synchronized Assessment of Bridge Structural Damage and Moving Force via Truncated Load Shape Function

Zhong

Xiang

Cheng³

2022

Applied Sciences

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Moving load and structural damage assessment has always been a crucial topic in bridge health monitoring, as it helps analyze the daily operating status of bridges and provides fundamental information for bridge safety evaluation. However, most studies and research consider these issues as two separate problems. In practice, unknown moving loads and damage usually coexist and influence the bridge vibration synergically. This paper proposes an innovative synchronized assessment method that determines structural damages and moving forces simultaneously. The method firstly improves the virtual distortion method, which shifts the structural damage into external virtual forces and hence transforms the damage assessment as well as the moving force identification to a multi-force reconstruction problem. Secondly, a truncated load shape function (TLSF) technique is developed to solve the forces in the time domain. As the technique smoothens the pulse function via a limited number of TLSF, the singularity and dimension of the system matrix in the force reconstruction is largely reduced. A continuous beam and a three-dimensional truss bridge are simulated as examples. Case studies show that the method can effectively identify various speeds and numbers of moving loads, as well as different levels of structural damages. The calculation efficiency and robustness to white noise are also impressive.

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Section: Introductionmentioning

confidence: 99%

Synchronized Assessment of Bridge Structural Damage and Moving Force via Truncated Load Shape Function

Zhong

Xiang

Cheng³

2022

Applied Sciences

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“…Recently, many researchers successfully used the learning ability of ANNs to quantify damage levels. Nguyen et al (2020b) were successful in proposing a novel approach to determine multiple cracks in a beam under a moving load. This method is a combination of wavelet analysis and deep learning.…”

Section: Introductionmentioning

confidence: 99%

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Nguyen

Roeck

et al. 2021

J. Zhejiang Univ. Sci. A

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Over recent decades, the artificial neural networks (ANNs) have been applied as an effective approach for detecting damage in construction materials. However, to achieve a superior result of defect identification, they have to overcome some shortcomings, for instance slow convergence or stagnancy in local minima. Therefore, optimization algorithms with a global search ability are used to enhance ANNs, i.e. to increase the rate of convergence and to reach a global minimum. This paper introduces a two-stage approach for failure identification in a steel beam. In the first step, the presence of defects and their positions are identified by modal indices. In the second step, a feedforward neural network, improved by a hybrid particle swarm optimization and gravitational search algorithm, namely FNN-PSOGSA, is used to quantify the severity of damage. Finite element (FE) models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method. For comparison, a traditional ANN is also used to estimate the severity of the damage. The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.

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“…e extraction of information from the structure's vibration measurement signals is quite popular for short-term studies [1][2][3]. e actual vibration signals are often utilized in the time domain so as to evaluate the signal change corresponding to time.…”

Section: Introductionmentioning

confidence: 99%

Separation of the Structure Signal by the Maximal Overlap Discrete Wavelet Transform and Fast Fourier Transform

Nguyen

2021

Advances in Materials Science and Engineering

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Power spectral density (PSD) is used for evaluating a structure’s vibration process. Moreover, PSD not only shows a discrete form of vibration but also presents various components in a vibration structure. The superposition of multiple vibration patterns on the same spectrum poses difficulty in analyzing the spectral information in the way needed to find the structure’s discrete vibration. This paper proposes a method for separating the synthetic vibration signal into a structure’s discrete vibration and other extraneous vibrations using the maximal overlap discrete wavelet transform (MODWT) method combined with the method of fast Fourier transform (FFT). With the combination of these two algorithms, MODWT and FFT, the signals of the synthesized vibration have been separated into component signals with different frequency ranges. This means that PSD will be formed, which is based on the combination of the single power spectra of the component signals. Thus, the single spectrum of each of these constructed components can be used to evaluate the types of discrete vibrations coexisting in a structure’s vibration process. The survey results in this paper show the sensitivity and suitability of select types of discrete vibrations to be separated out during the assessment of the structural change, so as to achieve the best possible plan for eliminating the unwanted and unexpected noise and vibration components.

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A data-driven approach based on wavelet analysis and deep learning for identification of multiple-cracked beam structures under moving load

Cited by 38 publications

References 54 publications

Synchronized Assessment of Bridge Structural Damage and Moving Force via Truncated Load Shape Function

Synchronized Assessment of Bridge Structural Damage and Moving Force via Truncated Load Shape Function

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Separation of the Structure Signal by the Maximal Overlap Discrete Wavelet Transform and Fast Fourier Transform

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