The use of frequency ratios to diagnose structural damage in varying environmental conditions

Surace, Cecilia; Bovsunovsky, A.

doi:10.1016/j.ymssp.2019.106523

Cited by 23 publications

(17 citation statements)

References 15 publications

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“…On the contrary, the selection of Mathematical Problems in Engineering appropriate transducer manufacturers, types, and materials can reduce this change [62]. Surace and Bovsunovsky [63] have mentioned that by increasing the temperature from −20 to 50°C, Young's modulus of metallic structures decreased almost linearly by 1%. erefore, it is presumed that the impact of temperature on the standard observations of metal structures can be ignored.…”

Section: Resultsmentioning

confidence: 99%

“…However, the effect of change in moisture content on wave propagation is less important in metallic structures as compared to the composite features since moisture influences the adhesive and transducer layer in metallic structures. As stated in Surace and Bovsunovsky [63], "as the value of Young's modulus for steel is imperceptibly dependent on the presence of rain or humidity of air, it can be presumed that natural frequencies of steel structures do not depend that much on those factors in comparison with natural frequencies of concrete bridge structures." Erazo et al [65] recommended that specific environmental impacts tend to elevate changes that influence all the vibration features in a standard process.…”

Section: Resultsmentioning

confidence: 99%

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Simulation and Analysis with Wavelet Transform Technique and the Vibration Characteristics for Early Revealing of Cracks in Structures

Abu‐Hamdeh

Daqrouq

Mebarek‐Oudina

2021

Mathematical Problems in Engineering

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Implementation of improved instruments is used to detect damage in an accurate manner and fully analyze its characteristics. An aluminum beam has been used in this work to identify cracks by using a vibration technique. The simulation of frequency response feature was conducted using a finite element model to provide average measures of intensities of vibration. Two forms of wavelet packet transform (WPT) entropies Shannon and log energy were applied to identify the position, width, and size of the crack. The results showed that with an increase in crack depth, the amplitude also increased at certain crack sizes and for all crack positions. For two crack depths of 1.6 mm and 0.16 mm having the same crack size and position 12 mm and 60 mm, respectively, a 4.5% increase in amplitude was observed at a crack depth of 1.6 mm. Moreover, the amplitude varied inversely with the position. A 12.6% increase in amplitude was observed at a crack depth of 1.6 mm rather than 0.16 mm, while both depths occurred at the same crack position (75 mm) and size (20 mm). Experimental validation was performed on a cantilever beam with one crack. The maximum absolute error found was 7.5% for the crack position and 9.1% for the crack size. With the increase in crack depth, the obtained results decrease the stiffness of a beam in a single crack case.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Simulation and Analysis with Wavelet Transform Technique and the Vibration Characteristics for Early Revealing of Cracks in Structures

Abu‐Hamdeh

Daqrouq

Mebarek‐Oudina

2021

Mathematical Problems in Engineering

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“…As for data points that fell inside the control limits in Figure 20(b), the situation occasionally appeared in damage detection of structures that exposed to the real environment (e.g. Z24 bridge), 61,62 since there still exist other uncertainties affecting the measurements or damage detection performance. To alleviate the problem in real applications, more data are required to construct the training dataset to encompass varied behavior of the structure in different performance regimes.…”

Section: Resultsmentioning

confidence: 99%

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

Shang

Sun

Xia

et al. 2020

Structural Health Monitoring

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One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

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“…During the operation of structures, periodic control of deformation deviations from the design values and static monitoring of deformations are performed [ 1 , 2 , 3 , 4 , 5 , 6 ]. There are several traditional methods for assessing stresses, e.g., the method of drilling holes, the method for determining stresses by measuring the surface hardness [ 7 ], methods using surface waves [ 8 , 9 ], the phenomenon of acoustoelasticity and interferometry [ 10 , 11 ], X-ray methods [ 12 ], electromagnetic methods [ 13 ], tensometric methods [ 14 ], magnetic resonance methods [ 15 ], and acoustic emission [ 16 ].…”

Section: Relative Workmentioning

confidence: 99%

An Acoustic Emission Method for Assessing the Degree of Degradation of Mechanical Properties and Residual Life of Metal Structures under Complex Dynamic Deformation Stresses

2021

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An acoustic emission method for assessing the degree of degradation of mechanical properties under conditions of complex dynamic deformation stresses is proposed. It has been shown that changing the operating conditions of metal structures, peak loads, external collisions, and thermally changing loads, which cannot be taken into account, leads to uncertainty and unpredictable structural changes in the material. This in turn makes it difficult to identify the state of the structure material to ensure trouble-free operation of the equipment. Changes in the mechanical properties under difficult loading conditions are identified by polynomial approximation of the results of AE measurements and the construction of boundary curves separating the operability region from the fracture region. This is achieved by approximating the experimental dependences of the acoustic parameters for various types of loading. This approach significantly expands the capabilities of the technical means of identification systems of metal structures, and in particular, allows the current state of the equipment and its suitability for further operation to be assessed without stopping the equipment in real time. It is of interest not only to fix the damage, but also to diagnose the processes of reducing the mechanical properties during the operation of the equipment.

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The use of frequency ratios to diagnose structural damage in varying environmental conditions

Cited by 23 publications

References 15 publications

Simulation and Analysis with Wavelet Transform Technique and the Vibration Characteristics for Early Revealing of Cracks in Structures

Simulation and Analysis with Wavelet Transform Technique and the Vibration Characteristics for Early Revealing of Cracks in Structures

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

An Acoustic Emission Method for Assessing the Degree of Degradation of Mechanical Properties and Residual Life of Metal Structures under Complex Dynamic Deformation Stresses

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