• Smart Structures and Systems
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• 제6권4호
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• pp.349-362
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• 2010

Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a PXI platform that controls the generation and measurement of the ultrasonic signals by means of piezoelectric transducers made of Lead Zirconate Titanate. Although the approach is demonstrated in a beam test, it is argued that the proposed method is general and applicable to any structure that can sustain the propagation of UGWs.

• Smart Structures and Systems
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• 제11권6호
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• pp.589-604
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• 2013

Identification of damage has become an evolving area of research over the last few decades with increasing the need of online health monitoring of the large structures. The visual damage detection can be impractical, expensive and ineffective in case of large structures, e.g., offshore platforms, offshore pipelines, multi-storied buildings and bridges. Damage in a system causes a change in the dynamic properties of the system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such good sensitive indication of structural damage. Identification of damaged jacket type offshore platform members, based on wavelet packet transform is presented in this paper. The jacket platform is excited by simple wave load. Response of actual jacket needs to be measured. Dynamic signals are measured by finite element analysis result. It is assumed that this is actual response of the platform measured in the field. The dynamic signals first decomposed into wavelet packet components. Then eliminating some of the component signals (eliminate approximation component of wavelet packet decomposition), component energies of remained signal (detail components) are calculated and used for damage assessment. This method is called Detail Signal Energy Rate Index (DSERI). The results show that reduced wavelet packet component energies are good candidate indices which are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and are applicable for finding damages' location.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.601-606
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• 2005

Welding deformations are affected by various factors. This research investigates effects of welding sequence and self-weight on welding deformation. According to the results by equivalence load method, magnitude of welding deformation with self-weight is about twice one without self-weight on parallel weld path component. But welding deformation with the components used in this research are not affected by welding sequence

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2011년도 정기 학술대회
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• pp.278-281
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• 2011

The global coordinates generated from Isomap algorithm provide a simple way to analyze and manipulate high dimensional observations in terms of their intrinsic nonlinear degrees of freedom. Thus, Isomap can find globally meaningful coordinates and nonlinear structure of complex data sets, while neither principal component analysis (PCA) nor multidimensional scaling (MDS) are successful in many cases. It is demonstrated that the adapted Isomap algorithm successfully enhances the quality of pattern classification for damage identification in various numerical examples.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.915-916
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• 2010

• Structural Engineering and Mechanics
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• 제6권8호
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• pp.901-919
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• 1998

Most reliability-based analyses focus on the reliability of the individual components of a structure. There are many advantages to examining the components in combination as an entire structural system. This paper illustrates an algorithm used in the computer program RELSYS (RELiability of SYStems) which computes the system reliability of any structure which can be modeled as a series-parallel combination of its components. A first-order method is used to initially compute the reliability of each individual component. The system reliability is computed by successively reducing the series and parallel systems until the system has been simplified to a single equivalent component. Equivalent alpha vectors are used to account for the correlation between failure modes during the system reduction process.

• Smart Structures and Systems
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• 제6권9호
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• pp.1041-1056
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• 2010

This paper investigates the possibility of a strategy for an automatic full recover of a structural component undergoing loading-unloading (fatigue) cycles: full recover means here that no replacement is required at the end of the mission. The goal is to obtain a material capable of self healing earlier before the damage becomes irreversible. Attention is focused on metallic materials, and in particular on shape memory alloys, for which the recovering policy just relies on thermal treatments. The results of several fatigue tests are first reported to acquire a deep understanding of the physical process. Then, for cycles of constant amplitude, the self-healing objective is achieved by mounting, on the structural component of interest, a suitable microcontroller. Its input, from suitable sensors, covers the current stress and strain in the alloy. The microcontroller elaborates from the input the value of a decisional parameter and activates the thermal process when a threshold is overcome.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2011년도 정기 학술대회
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• pp.32-35
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• 2011

전기역학적 임피던스(electromechanical impedance)를 이용한 구조물 건전성 모니터링(structural health monitoring; SHM) 기술은 구조물의 주요 부재에 압전센서를 부착하여 이로부터 획득한 임피던스 신호의 변화를 관찰함으로써 구조물의 국부적 상태를 실시간으로 진단하는 것이다. 임피던스는 손상뿐만 아니라 외부 온도에도 민감하게 반응하기 때문에 구조물 진단 결과에 상당한 오차를 유발할 수 있으므로 이에 대한 보상을 수행해야 한다. 따라서 본 논문에서는 온도변화가 임피던스 기반 진단 결과에 미치는 영향을 PZT 센서를 사용하여 실험적으로 연구하였다. 리액턴스(reactance)의 주성분 분석(Principal Component Analysis; PCA)을 통해 도출된 첫번째 주성분과 저항(resistance)으로부터 계산된 손상지수 사이의 관계를 분석함으로써, 온도변화에 의해 구별되지 않았던 손상을 보다 확연하게 구별 할 수 있음을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• Smart Structures and Systems
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• 제6권1호
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• pp.39-56
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• 2010

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.