• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.8
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• pp.870-879
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• 2007

In this study, various damage modes in bending unimorph piezoelectric composite actuators with a thin sandwiched PZT plate during bending fracture tests have been evaluated by monitoring acoustic emission (AE) signals in terms of waveform and peak frequency as well as AE parameters. Three kinds of actuator specimens consisting of woven fabric fiber skin layers and a PZT ceramic core layer are loaded with a roller and an AE activity from the specimen is monitored during the entire loading using an AE transducer mounted on the specimen. AE characteristics from a monolithic PZT ceramic with a thickness of $250{\mu}m$ are examined first in order to distinguish different AE signals from various possible damage modes in piezoelectric composite actuators. Post-failure observations and stress analyses in the respective layers of the specimens are conducted to identify particular features in the acoustic emission signal that correspond to specific types of damage modes. As a result, the signal classification based on waveform and peak frequency analyses successfully describes the failure process of the bending piezoelectric composite actuator exhibiting diverse failure mechanisms. Furthermore, it is elucidated that when the PZT ceramic embedded actuators are loaded mechanical bending loads, the failure process of actuator specimens with different lay-up configurations is almost same irrespective of their lay-up configurations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.3 s.73
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• pp.313-322
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• 2006

In the genetic algoricm (GA) based damage detection methods using vibration of structures, the selection of modal properties is important to improve the accuracy of damage detection. The objective of this study is to improve the accuracy of damage detection using natural frequency and modal strain energy, The following approaches are used to achieve the goal. First, modal strain energy is formulated and a new GA-based damage detection technique using natural frequency and modal strain energy is proposed. Next, to verify the efficiency of proposed technique, damage scenarios for free-free beam are designed and vibration modal tests of the target structure are conducted. Finally, the feasibility of the proposed technique is verified in comparison with other GA-based damage detection technique using natural frequency and mode shape.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.119-127
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• 2006

A non-destructive time domain approach to examine structural damage using parameterized partial differential equations and Galerkin approximation techniques is presented. The time domain analysis for damage detection is independent of modal parameters and analytical models unlike frequency domain methods which generally rely on analytical models. The time history of the vibration response of the structure was used to identify the presence of damage. Damage in a structure causes changes in the physical coefficients of mass density, elastic modulus and damping coefficients. This is a part of our ongoing effort on the general problem of modeling and parameter estimation for internal damping mechanisms in a composite beam. Namely, in detecting damage through time-domain or frequency-domain data from smart sensors, the common damages are changed in modal properties such as natural frequencies, mode shapes, and mode shape curvature. This paper examines the use of beam-like structures with piezoceramic sensors and actuators to perform identification of those physical parameters, and detect the damage. Experimental results are presented from tests on cantilevered composite beams damaged at different locations and different dimensions. It is demonstrated that the method can sense the presence of damage and obtain the position of a damage.

• Steel and Composite Structures
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• v.14 no.4
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.1-15
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• 2023

This study conducts uniaxial compression tests on intact and single crack-contained rocks to investigate the time-frequency domain characteristics of acoustic emission (AE) signals monitored during the deformation failure process. A processing approach, short-time Fourier transform (STFT), is performed to obtain the evolution characteristics of time-frequency domain of AE signals. The AE signal modes at different deformation stages of rocks are different. Five modes of AE signal are observed during the cracking process of rocks. The evolution characteristics of time-frequency domain of AE signals processed by STFT can be utilized to evaluate the damage process of rocks. The difference of time-frequency domain characteristics between intact and cracked rocks is comparatively analyzed. The distribution characteristics of frequency changing from a single band-shaped cluster to multiple band-shaped clusters can be regarded as an early warning information of damage and failure of rocks. Meanwhile, the attenuation of frequency enables the exploration of rock failure trends.

• Structural Engineering and Mechanics
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• v.18 no.6
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• pp.709-729
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• 2004

This paper presents numerical and experimental investigations on damage detection of mono-coupled multistory buildings using natural frequency as only diagnostic parameter. Frequency equation of a mono-coupled multistory building is first derived using the transfer matrix method. Closed-form sensitivity equation is established to relate the relative change in the stiffness of each story to the relative changes in the natural frequencies of the building. Damage detection is then performed using the sensitivity equation with its special features and minimizing the norm of an objective function with an inequality constraint. Numerical and experimental investigations are finally conducted on a mono-coupled 3-story building model as an application of the proposed algorithm, in which the influence of modeling error on the degree of accuracy of damage detection is discussed. A mono-coupled 10-story building is further used to examine the capability of the proposed algorithm against measurement noise and incomplete measured natural frequencies. The results obtained demonstrate that changes in story stiffness can be satisfactorily detected, located, and quantified if all sensitive natural frequencies to damaged stories are available. The proposed damage detection algorithm is not sensitive to measurement noise and modeling error.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• Journal of Information Technology Applications and Management
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• v.15 no.2
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• pp.67-82
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• 2008

Risk that breed large size disaster is happening variously for cause at social. natural a management. Incidence and damage scale are trend that increase rapidly than past. In these circumstance, to keep operational continuity of organization, area, society, risk management action that establish systematic counter measure estimating and analyze occurrence possibility and expectation damage of risk is essential indispensable issue and the best countermeasure. Risk management action does by main purpose establish optimum disaster reduction countermeasure. To deduce various countermeasure, process that estimate and analyze occurrence possibility and expectation damage of risk is essential indispensable issue. Therefore, this paper studies process design that can presume risk occurrence frequency and damage scale through distribution of probability.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.15-16
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• 2012

The estimation of crack damages is studied using the radio frequency system and tilt switch sensors in RC beams. If load is received on the center of the flexible specimen, sensor housing using cement mortar of the flexible specimen will be destroyed, and these are become to send signals of damages at the radio frequency system connected with tilt switch sensors. This study is fundamental research for the estimation of the damage using tilt switch sensors in RC beams.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.4
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• pp.33-37
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• 2007

Creep failure of 316LN stainless steel (SS) occurs due to the nucleation and growth of cracks. An investigation was performed to correlate the creep damage with ultrasonic wave speeds and angular frequencies using creep-tested 316LN SS specimens. Ultrasonic wave measurements were made in the direction of and perpendicular to the loading using contact probes with central frequencies of 10, 15, and 20 MHz. We found that the angular frequency and wave speed decreased with increasing creep time to rupture by analyzing the ultrasonic signals from the 15 and 20 MHz probes. Therefore, the creep damage was sensitive to the angular frequency and wave speed of ultrasonic waves.