• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.225-231
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• 2006

The purpose of this study is to develop a promising hybrid structural health monitoring system for structural joints. For this propose, the combined use of vibration-based techniques and electro-mechanical impedance technique is employed. For the verification of the proposed health monitoring scheme, a series of damage scenarios are designed to simulate various situations at which the connection joints can experience during their service life. The obtained experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the connecting states and the target damage locations. From the analysis. it is shown that the proposed hybrid health monitoring system is successful for acquiring global and local damage information on the structural joints.

• Structural Monitoring and Maintenance
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• 제7권2호
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• pp.149-166
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• 2020

This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.870-874
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• 2004

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by Piezoelectric impedance-based technique associated with longitudinal wave propagation method. The bolt fastening condition is adjusted by torque wrench. In order to estimate the damage condition numerically, three damage indices, impedance peak frequency shift ${\Delta}F$, peak amplitude ratio $\delta$ and quality factor ratio $\gamma$, are proposed in this paper. Furthermore, an assessment method is described for estimation of the damage by using these three damage indices.

• 한국전산구조공학회논문집
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• 제25권1호
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• pp.91-99
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• 2012

본 연구에서는 PZT 소자의 정압전 효과에 의한 동적 응답신호를 이용하는 보 구조물 손상 모니터링 기법을 제안하였다. 특히, 모드 변형에너지기반 보 구조물 손상 모니터링에 PZT 정압전 응답신호를 입력자료로 활용하는 방안에 대한 연구에 주안점이 있다. 먼저, PZT 소자의 정압전 효과 및 동적 변형률 응답의 이론적 배경을 요약하였다. 다음으로, 모드 변형에너지기반 보 구조물 손상위치 모니터링 기법을 제시하였다. 제시된 기법의 적합성을 검증하기 위해, 캔틸레버 보 모형을 대상으로 강제진동 실험을 수행하였으며, 세 종류의 센서(가속도계, PZT 센서, 변형률계)를 통해 동적 응답신호가 계측되었다. 손상 전후에 계측된 이들 진동신호들을 사용하여 모드 변형에너지기반의 손상위치 모니터링이 수행되었다.

• Earthquakes and Structures
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• 제12권6호
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• pp.673-687
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• 2017

A three-dimensional; structural health monitoring; vertical; planar; cross-sample entropy; multiscaleA three-dimensional structural health monitoring (SHM) system based on multiscale entropy (MSE) and multiscale cross-sample entropy (MSCE) is proposed in this paper. The damage condition of a structure is rapidly screened through MSE analysis by measuring the ambient vibration signal on the roof of the structure. Subsequently, the vertical damage location is evaluated by analyzing individual signals on different floors through vertical MSCE analysis. The results are quantified using the vertical damage index (DI). Planar MSCE analysis is applied to detect the damage orientation of damaged floors by analyzing the biaxial signals in four directions on each damaged floor. The results are physically quantified using the planar DI. With progressive vertical and planar analysis methods, the damaged floors and damage locations can be accurately and efficiently diagnosed. To demonstrate the performance of the proposed system, performance evaluation was conducted on a three-dimensional seven-story steel structure. According to the results, the damage condition and elevation were reliably detected. Moreover, the damage location was efficiently quantified by the DI. Average accuracy rates of 93% (vertical) and 91% (planar) were achieved through the proposed DI method. A reference measurement of the current stage can initially launch the SHM system; therefore, structural damage can be reliably detected after major earthquakes.

• Smart Structures and Systems
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• 제15권3호
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• pp.523-541
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• 2015

Structural health monitoring of steel truss bridge based on changes in modal properties was investigated in this study. Vibration measurements with five sensors were conducted at an existing Warren truss bridge with partial fractures in diagonal members before and after an emergency repair work. Modal properties identified by the Eigensystem Realization Algorithm showed evidences of increases in modal damping due to the damage in diagonal member. In order to understand the dynamic behavior of the bridge and possible mechanism of those increases in modal damping, theoretical modal analysis was conducted with three dimensional frame models. It was found that vibrations of the main truss could be coupled internally with local vibrations of diagonal members and the degree of coupling could change with structural changes in diagonal members. Additional vibration measurements with fifteen sensors were then conducted so as to understand the consistency of those theoretical findings with the actual dynamic behavior. Modal properties experimentally identified showed that the damping change caused by the damage in diagonal member described above could have occurred in a diagonal-coupled mode. The results in this study imply that damages in diagonal members could be detected from changes in modal damping of diagonal-coupled modes.

• Smart Structures and Systems
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• 제30권3호
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• pp.263-271
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• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.291-308
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• 2013

A novel concept of transmissibility based on a wavelet transform for structural damage detection is presented in this paper. The main objective of the research was the development of a method for detecting slight damage at the incipient stage. As a vibration-based approach, the concept of transmissibility has attracted considerable interest because of its advantages and effectiveness in damage detection. However, like other vibration-based methods, transmissibility-based approaches suffer from insensitivity to slight local damage because of the regularity of the traditional Fourier transform. Therefore, the powerful signal processing techniques must be found to solve this problem. Wavelet transform that is able to capture subtle information in measured signals has received extensive attention in the field of damage detection in recent decades. In this paper, we first propose a novel transmissibility concept based on the wavelet transform. Outlier analysis was adopted to construct a damage detection algorithm with wavelet-based transmissibility. The feasibility of the proposed method was numerically investigated with a typical six-degrees-of-freedom spring-mass system, and comparative investigations were performed with a conventional transmissibility approach. The results demonstrate that the proposed transmissibility is more sensitive than conventional transmissibility, and the former is a promising tool for structural damage detection at the incipient stage.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.