• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 봄 학술발표회 논문집
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• pp.366-373
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• 2003

This paper introduces a frequency-domain method of structural damage identification. It is formulated in a general form to include the nonlinearity of damage magnitudes from the dynamic stiffness equation of motion for a beam structure. The appealing features of the present damage identification method are: (1) it requires only the frequency response functions measured from damaged structure as the input data, and (2) it can locate and quantify many local damages at the same time. The feasibility of the present damage identification method is tested through some numerically simulated damage identification analyses for a cantilevered beam with three piece-wise uniform damages.

• Smart Structures and Systems
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• 제14권2호
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Structural Engineering and Mechanics
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• 제40권5호
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• pp.719-743
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• 2011

A hybrid damage monitoring scheme using parallel acceleration-impedance approaches is proposed to detect girder damage and support damage in steel plate-girder bridges which are under ambient train-induced excitations. The hybrid scheme consists of three phases: global and local damage monitoring in parallel manner, damage occurrence alarming and local damage identification, and detailed damage estimation. In the first phase, damage occurrence in a structure is globally monitored by changes in vibration features and, at the same moment, damage occurrence in local critical members is monitored by changes in impedance features. In the second phase, the occurrence of damage is alarmed and the type of damage is locally identified by recognizing patterns of vibration and impedance features. In the final phase, the location and severity of the locally identified damage are estimated by using modal strain energy-based damage index methods. The feasibility of the proposed scheme is evaluated on a steel plate-girder bridge model which was experimentally tested under model train-induced excitations. Acceleration responses and electro-mechanical impedance signatures were measured for several damage scenarios of girder damage and support damage.

• Structural Engineering and Mechanics
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• 제25권1호
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• pp.39-52
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• 2007

This paper describes an experimental study on structural health monitoring of a 1:3-scaled one-story concrete frame subjected to seismic damage and retrofit. The structure is tested on a shaking table by exerting successively enhanced earthquake excitations until severe damage, and then retrofitted using fiber-reinforced polymers (FRP). The modal properties of the tested structure at trifling, moderate, severe damage and strengthening stages are measured by subjecting it to a small-amplitude white-noise excitation after each earthquake attack. Making use of the measured global modal frequencies and a validated finite element model of the tested structure, a neural network method is developed to quantitatively identify the stiffness reduction due to damage and the stiffness enhancement due to strengthening. The identification results are compared with 'true' damage severities that are defined and determined based on visual inspection and local impact testing. It is shown that by the use of FRP retrofit, the stiffness of the severely damaged structure can be recovered to the level as in the trifling damage stage.

• 한국해양공학회지
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• 제24권3호
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• pp.1-10
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• 2010

In this study, hybrid smart sensor nodes were developed for the autonomous structural health monitoring of prestressed concrete (PSC) girders. In order to achieve the objective, the following approaches were implemented. First, we show how two types of smart sensor nodes for the hybrid health monitoring were developed. One was an acceleration-based smart sensor node using an MEMS accelerometer to monitor the overall damage in concrete girders. The other was an impedance-based smart sensor node for monitoring the local damage in prestressing tendons. Second, a hybrid monitoring algorithm using these smart sensor nodes is proposed for the autonomous structural health monitoring of PSC girders. Finally, we show how the performance of the developed system was evaluated using a lab-scaled PSC girder model for which dynamic tests were performed on a series of prestress-loss cases and girder damage cases.

• Smart Structures and Systems
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• 제21권6호
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• pp.741-749
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• 2018

Structural health monitoring (SHM) systems are necessary to achieve smart predictive maintenance and repair planning as well as they lead to a safe operation of mechanical structures. In the context of vibration-based SHM the measured structural responses are employed to draw conclusions about the structural integrity. This usually leads to a mathematically illposed inverse problem which needs regularization. The restriction of the solution set of this inverse problem by using prior information about the damage properties is advisable to obtain meaningful solutions. Compared to the undamaged state typically only a few local stiffness changes occur while the other areas remain unchanged. This change can be described by a sparse damage parameter vector. Such a sparse vector can be identified by employing $L_1$-regularization techniques. This paper presents a novel framework for damage parameter identification by combining sparse solution techniques with an Extended Kalman Filter. In order to ensure sparsity of the damage parameter vector the measurement equation is expanded by an additional nonlinear $L_1$-minimizing observation. This fictive measurement equation accomplishes stability of the Extended Kalman Filter and leads to a sparse estimation. For verification, a proof-of-concept example on a quadratic aluminum plate is presented.

• 한국지진공학회논문집
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• 제1권2호
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• pp.1-15
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• 1997

본 논문에서는 대형구조물에서 구조물의 안전성 평가와 관련하여 구조물이 국부손상도를 추정하기 위한 효율적인 부분구조추정(Substructural Identification) 기법에 대하여 연구하였다. 먼저, 부분구조 추정법을 위한 모형식을 설정하기 위하여 운동방정식으로부터 부분구조에 대한 계측오차를 처리하기 위한 모형을 포함한 추계론적 자동회귀-이동평균(ARAMX) 모형식을 유도하였다. 추정된 모형식의 계수는 유도된 관계식을 이용하면, 구조손상 평가에 이용될 수 있는 강성행렬로 환산될 수 있다. 본 논문에서 유도된 부분구조 추정법의 가장 큰 장점은 매우 안정되고 정확도가 우수한 구조추정법인 ARMAX 모형식에 기반한 순차적 예측오차 방법을 사용함으로써 다른 방법에 비해 추정의 안정성 및 정확도가 뛰어나다는 것이다. 다음으로는 개발된 부분구조 추정법을 이용하여 구조 손상도 추정이 수행되었다. 손상도 추정을 위하여 앞서 순차적 예측오차 방법을 이용하여 추정된 구조계 현상태의 강성행렬을 바탕으로, 최소지승법을 이용하여 구하는 간접법이 제시되었다. 제시된 방법들의 검증을 위하여 예제해석이 수행되었다. 트러스 및 연속교 모형 그리고 실험적 예제에 적용하여 구조의 강성행렬 및 감쇠행렬을 추정하였다. 이를 바탕으로 손상도 추정방법이 검증되었다. 해석결과로부터, 개발된 방법이 효율적이고 정확도 및 안정성의 측면에서 우수한 성질이 있음을 확인할 수 있다.

• 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.

• Smart Structures and Systems
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• 제22권6호
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• pp.699-709
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• 2018

With increasing traffic volumes and rising vehicle traffic, especially in cities, the number of pedestrian bridges has also increased significantly. Like all other structures, pedestrian bridges also suffer damage. In order to increase the safety of pedestrians, it is necessary to identify existing damage and to repair them to ensure the safety of the bridge structures. Owing to the shortcomings of local methods in identifying damage and in order to enhance the reliability of detection and identification of structural faults, signal methods have seen significant development in recent years. In this research, a new methodology, based on cone-shaped kernel distribution with a new damage index, has been used for damage detection in pedestrian truss bridges. To evaluate the proposed method, the numerical models of the Warren Type steel truss and the Arregar steel footbridge were used. Based on the results, the proposed method and damage index identified the damage and determined its location with a high degree of precision. Given the ease of use, the proposed method can be used to identify faults in pedestrian bridges.

• Structural Engineering and Mechanics
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• 제64권3호
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• pp.353-360
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• 2017

This study presents the Big Bang and Big Crunch (BB-BC) optimization algorithm for detection of structure damage in large severity. Local damage is represented by a perturbation in the elemental stiffness parameter of the structural finite element model. A nonlinear objective function is established by minimizing the discrepancies between the measured and calculated acceleration responses (AR) of the structure. The BB-BC algorithm is utilized to solve the objective function, which can localize the damage position and obtain the severity of the damage efficiently. Numerical simulations have been conducted to identify both single and multiple structural damages for beam, plate and European Space Agency Structures. The present approach gives accurate identification results with artificial measurement noise.