• Interaction and multiscale mechanics
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• v.5 no.3
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• pp.169-185
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• 2012

The concrete bridge is likely to produce fatigue cracks during long period of service due to the moving vehicular loads and the degeneration of materials. This paper deals with the time-frequency analysis of a coupled bridge-vehicle system. The bridge is modeled as an Euler beam with breathing cracks. The vehicle is represented by a two-axle vehicle model. The equation of motion of the coupled bridge-vehicle system is established using the finite element method, and the Newmark direct integration method is adopted to calculate the dynamic responses of the system. The effect of breathing cracks on the dynamic responses of the bridge is investigated. The time-frequency characteristics of the responses are analyzed using both the Hilbert-Huang transform and wavelet transform. The results of time-frequency analysis indicate that complicated non-linear and non-stationary features will appear due to the breathing effect of the cracks.

• Structural Engineering and Mechanics
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• v.60 no.5
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• pp.891-902
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• 2016

The beam structure with breathing cracks subjected to harmonic excitations was modeled by FEM based on Euler-Bernoulli theory, and a piecewise dynamical system was deduced. The precise integration method (PIM) was employed to propose an algorithm for analyzing the dynamic responses of the deduced system. This system was first divided into linear sub-systems, between which there are switching points resulted from the breathing cracks. The inhomogeneous terms due to the external excitations were tackled by introducing auxiliary variables to express the harmonic functions, hence the sub-systems are homogeneous. The PIM was then applied to solve the homogeneous sub-systems one by one. During the procedures, a predictor-corrector algorithm was presented to determine the switching points accurately. The presented method can provide solutions with an accuracy to a magnitude of $10^{-12}$ compared with exact solutions obtained by the theories of ordinary differential equations. The PIM results are much more accurate than Newmark ones with the same time step. Moreover, it is found that the PIM can maintain a high level of accuracy even when the time step increases within a relatively wide range.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.309-322
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• 2014

Detection of fatigue cracks at an early stage of their development is important in structural health monitoring. The breathing of cracks in a structure generates higher harmonic components of the exciting frequency in the frequency spectrum. Previously, the residual operational deflection shape (R-ODS) method was successfully applied to beams with a single crack. The method is based on the ODSs at the exciting frequency and its higher harmonic components which consider both amplitude and phase information of responses to map the deflection pattern of structures. Although the R-ODS method shows the location of a single crack clearly, its identification for the location of multiple cracks in a structure is not always obvious. Therefore, an improvement to the R-ODS method is presented here to make the identification process distinct for the beams with multiple cracks. Numerical and experimental examples are utilised to investigate the effectiveness of the improved method.

• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.33-52
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• 2017

A cantilever beam with a breathing crack is studied to improve the breathing crack identification sensitivity by the parametric optimization of sample entropy and wavelet transformation. Crack breathing is a special bi-linear phenomenon experienced by fatigue cracks which are under dynamic loadings. Entropy is a measure, which can quantify the complexity or irregularity in system dynamics, and hence employed to quantify the bi-linearity/irregularity of the vibration response, which is induced by the breathing phenomenon of a fatigue crack. To improve the sensitivity of entropy measurement for crack identification, wavelet transformation is merged with entropy. The crack identification is studied under different sinusoidal excitation frequencies of the cantilever beam. It is found that, for the excitation frequencies close to the first modal frequency of the beam structure, the method is capable of detecting only 22% of the crack depth percentage ratio with respect to the thickness of the beam. Using parametric optimization of sample entropy and wavelet transformation, this crack identification sensitivity is improved up to 8%. The experimental studies are carried out, and experimental results successfully validate the numerical parametric optimization process.

• Structural Engineering and Mechanics
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• v.23 no.1
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• pp.1-13
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• 2006

Structural cracks may cause variations in structural stiffness and thus produce bilinear vibrations to structures. This study examines the dynamic behavior of structures with breathing cracks. A generalized algorithm based on the finite element method and bilinear theory was developed to study the influence of a breathing crack on the vibration characteristic. All the formulae derived in the time domain were applied to estimate the period of the overall bilinear motion cycle, and the contact effect was considered in the calculations by introducing the penetration of the crack surface. Changes in the dynamic characteristics of cracked structures are investigated by assessing the variation of natural frequencies under different crack status in either the open or closed modes. Results in estimation with vibrational behavior variation are significant compared with the experimental results available in the literature as well as other numerical calculations.

• Smart Structures and Systems
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• v.25 no.4
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• pp.459-469
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• 2020

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.722-729
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• 2000

The goal of this paper is to describe an advanced method of a crack detection: a new way to localize position and to estimate depth of a crack on rotating shaft. As a first step, the shaft is physically modelled with a finite element method and the dynamic mathematical model is derived using the Hamilton principle; thus, the system is represented by various subsystems. The equations of motion of the shaft with a crack are established by adapting the local stiffness change through breathing and gaping from the crack to an undamaged shaft. This is the reference system for the given system. Based on a model for transient behavior induced from vibration measured at the bearings, a nonlinear state observer is designed to detect cracks on the shaft. This is the elementary NL-observer (Beo). Using the observer, an Estimator (Observer Bank) is established and arranged at the certain position on the shaft. When a crack position is localized, the procedure for estimating of the depth is engaged.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.224-225
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• 2018

Modern people are more interested in the indoor environment as they spend more time indoors than in the past. Among the air pollutants in the indoor air, ladon gas is a colorless, tasteless, odorless, inert gas produced by nuclear decomposition of naturally occurring uranium in rocks and soils. It has been proven that ladon gas is introduced into the room through cracks on the floor of the building or basement wall, and it causes various diseases such as lung cancer when exposed to radon during human breathing. The US Environmental Protection Agency (EPA) specifies 4pCi / L as a necessary measure for radon, and the Korea Environmental Protection Agency has implemented comprehensive indoor radon management measures since 2007. Therefore, in this study, we intend to adsorb and reduce radon in indoor air pollutants.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.111-112
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• 2017

Radon has been considered the greatest source of exposure within the total radiation exposure of the human body. xposure from radon, which exists in indoor air quality, lacks public perception, Radon, which exists anywhere on earth, is not regarded as a state of attention even if it is above the average level. Indoor radon exposure situations are not intentionally introduced, and essentially the attention and responsibilities of radon exposures are assumed to be in indoor occupants. So, these are caused by common uranium and thorium scattering on Earth, and are brought into the building by fine cracks or exposed indicators of the buildings. Therefore, this study aims to reduce the risk of radon rays and reduce radon, which induces diseases caused by breathing in the body of indoor air pollutants and emitting diseases by emitting alpha rays from the radon gas.

• Smart Structures and Systems
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• v.20 no.6
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• pp.669-682
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• 2017

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation.