• 한국지진공학회논문집
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• 제9권1호통권41호
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• pp.71-78
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• 2005

본 논문에서는 정유연성 지배주파수를 이용한 개선된 매개변수 인식기법이 제안되었다. 정유연성 지배주파수를 이용할 경우 주파수응답함수에서 고유주파수 보다 다수의 정보를 추출할 수 있어 매개변수 인식의 성능을 향상시킬 수 있는 장점이 있다. 정유연성 지배주파수를 매개변수 인식에 이용하기 위하여 기존의 고유주파수 민감도에 기반한 구조계 인식기법이 확장되었다. 정유연성 지배주파수의 이용을 통한 매개변수 인식의 성능향상은 수치예제를 통해 증명하였다. 수치예제는 스프링과 질량으로 이루어진 간략 모델이 사용되었으며, 고유주파수 만을 이용하여 구한 인식값과 비교한 결과 보다 정확한 매개변수 값의 인식이 가능함을 알 수 있었다.

• Smart Structures and Systems
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• 제30권4호
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• 대한기계학회논문집A
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• 제25권12호
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• 한국전산구조공학회논문집
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• 제22권5호
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• pp.403-410
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• 2009

본 연구에서는 실험결과를 바탕으로 액체형 기둥감쇠기의 감쇠계수와 제어력에 참여하는 유효질량을 시스템 식별기법을 이용하여 정량적으로 평가하였다. 먼저, 두가지 형태의 동조액체형 기둥감쇠기를 제작하여 진동대 실험을 실시하였다. 다음으로 수조내부 액체의 수위를 변화시켜 가면서 조화하중 가진실험에 의한 감쇠기의 동적특성을 실험적으로 구하였다. 마지막으로 수치해석 모델과 실험결과와의 오차를 최소화하는 것에 의해 수위변화에 따른 감쇠기의 감쇠비와 유효질량을 정량적으로 평가하였다. 시스템 식별결과로부터 감쇠비와 유효질량이 감쇠기의 수위가 증가함에 따라 감소하는 것으로 나타났다.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.393-410
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• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• Smart Structures and Systems
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• 제13권5호
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• pp.821-848
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• 2014

This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.2038-2045
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• 2021

The main objective of this work is to propose a reliable routine standard operation procedures (SOP) for structural health monitoring and diagnosis of nuclear power plants (NPPs). At present, NPPs have monitoring systems that can be used to obtain the quantitative health record of containment (CTMT) buildings through system identification technology. However, because the measurement signals are often interfered with by noise, the identification results may introduce erroneous conclusions if the measured data is directly adopted. Therefore, this paper recommends the SOP for signal screening and the required identification procedures to identify the dynamic characteristics of the CTMT of NPPs. In the SOP, three recommend methods are proposed including the Recursive Least Squares (RLS), the Observer Kalman Filter Identification/Eigensystem Realization Algorithm (OKID/ERA), and the Frequency Response Function (FRF). The identification results can be verified by comparing the results of different methods. Finally, a preliminary CTMT healthy record can be established based on the limited number of earthquake records. It can be served as the quantitative reference to expedite the restart procedure. If the fundamental frequency of the CTMT drops significantly after the Operating Basis Earthquake and Safe Shutdown Earthquake (OBE/SSE), it means that the restart actions suggested by the regulatory guide should be taken in place immediately.

• Structural Engineering and Mechanics
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• 제15권3호
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• pp.285-297
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• 2003

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto-regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto-regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.

• Structural Engineering and Mechanics
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• 제50권1호
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• pp.105-119
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• 2014

This study presents a steel container crane movement analysis and assessment based on structural health monitoring (SHM). The accelerometers are used to monitor the dynamic crane behavior and a 3-D finite element model (FEM) was designed to express the static displacement of the crane under the different load cases. The multi-input single-output nonlinear autoregressive neural network with external input (NNARX) model is used to identify the crane dynamic displacements. The FEM analysis and the identification model are used to investigate the safety and the vibration state of the crane in both time and frequency domains. Moreover, the SHM system is used based on the FEM analysis to assess the crane behavior. The analysis results indicate that: (1) the mean relative dynamic displacement can reveal the relative static movement of structures under environmental load; (2) the environmental load conditions clearly affect the crane deformations in different load cases; (3) the crane deformations are shown within the safe limits under different loads.