• Smart Structures and Systems
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• 제15권2호
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• pp.425-445
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• 2015

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

• 한국공간구조학회지
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• 제15권1호
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• pp.14-21
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• 2015

• Smart Structures and Systems
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• 제28권6호
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Structural Monitoring and Maintenance
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• 제4권1호
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• pp.33-52
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• 2017

A cantilever beam with a breathing crack is studied to improve the breathing crack identification sensitivity by the parametric optimization of sample entropy and wavelet transformation. Crack breathing is a special bi-linear phenomenon experienced by fatigue cracks which are under dynamic loadings. Entropy is a measure, which can quantify the complexity or irregularity in system dynamics, and hence employed to quantify the bi-linearity/irregularity of the vibration response, which is induced by the breathing phenomenon of a fatigue crack. To improve the sensitivity of entropy measurement for crack identification, wavelet transformation is merged with entropy. The crack identification is studied under different sinusoidal excitation frequencies of the cantilever beam. It is found that, for the excitation frequencies close to the first modal frequency of the beam structure, the method is capable of detecting only 22% of the crack depth percentage ratio with respect to the thickness of the beam. Using parametric optimization of sample entropy and wavelet transformation, this crack identification sensitivity is improved up to 8%. The experimental studies are carried out, and experimental results successfully validate the numerical parametric optimization process.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• 한국구조물진단유지관리공학회 논문집
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• 제8권2호
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• pp.137-145
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• 2004

본 연구에서는 구조성질들이 제대로 평가되지 않은 구조물의 부분구조 모델을 설정하는 방법을 제시한다. 본 연구에서는 부분구조 모델을 정의하기 위해 구조물의 대상 부분구조에서 계측한 구조거동치를 필요로 하며, 부분구조 모델은 부분구조 자체의 강성도변수와 경계스프링 계수를 추정하여 설정한다. 정적 및 주파수영역 SI(system identification) 기법들이 부분구조의 제한된 위치에서 측정된 거동치를 사용하여 적용되었다. 정적거동과 동적거동 계측 각각에 대한 시뮬레이션 연구가 수행되었으며, 연구결과 및 문제점들이 검토되었다. 시뮬레이션 연구에서 검증된 절차에 따라 이동트럭과 시공발파에 의한 동적거동 계측치를 사용하여 실제의 다경간 플레이트 거더 게르버교의 부분구조 모델 설정을 수행하였다.

• Structural Monitoring and Maintenance
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• 제7권3호
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• pp.195-213
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• 2020

In this paper an extension to the method for the identification of mechanical parameters of nonlinear systems proposed in Breccolotti and Materazzi (2007) for MDoF systems is presented. It can be used for damage identification purposes when damage modifies the linear characteristics of the investigated structure. It is based on the following two main features: the solution of the Fokker-Planck equation that describes the response probabilistic properties of the system when it is excited by external Gaussian loads; and a model updating technique that minimizes the differences between the response of the actual system and that of a parametric system used to identify the unknown parameters. Numerical analysis, that simulate virtual experimental tests, are used in the paper to show the capabilities of the method and to analyse the conditions required for its application.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
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• pp.136-143
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• 1995

The problem of the structural identification becomes important, particularly with relation to the rapid increase of the number of the damaged or deteriorated structures, such as highway bridges, buildings, and industrial facilities. This paper summarizes the recent studies related to those problems by the present authors. The system identfication methods are generally classified as the time domain and the frequency domain methods. As time doamin methods, the sequential algorithms such as the extended Kalman filter and the sequential prediction error method are studied. Several techniques for improving the convergences are incorporated. As frequency domain methods, a new frequency response function estimator is introduced. For damage estimation of existing structures, the modal perturbation and the sensitivity matrix methods are studied. From the example analysis, it has been found that the combined utilization of the measurement data for the static response and the dynamic (modal) properties are very effictive for the damage estimation.

• 전산구조공학
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• 제10권3호
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• pp.157-165
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• 1997

이 연구의 목적은 대형 구조물의 상설 감지를 위한 감지기의 최적 위치의 알고리즘을 개발하는데에 있다. 구조물의 진동을 이용한 감지 시스템은 장기적으로 계속해서 구조물을 자동으로 감지하는데에 좋은 방법중의 하나이다. 하지만 구조물의 진동을 정확히 계측하기 위해서는 감지기의 위치나 감지기의 숫자에 큰 영향을 받는데, 이와 같은 일은 대형 구조물에 있어서 쉽지가 않다. 최적의 감지기 위치와 최소의 감지기로 가장 정확한 데이터를 획득하기 위하여 최적합한 감지기의 위치를 위한 알고리즘이 개발되어 수치적 그리고 실험적으로 유용성을 보인다. EOT가 개발되어 모형 교량에 적용하여 EIM과 비교 분석된다. 이들의 비교를 통하여, 이 연구에서 제안되어진 EOT가 적은 수의 감지기로 좋은 결과를 보여, 상설감지의 목적에 적합함을 보여준다.

• Earthquakes and Structures
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• 제4권2호
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• pp.203-217
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• 2013

In recent decades, it has been realized that increasing the lateral stiffness of structure subjected to lateral loads is not the only parameter enhancing safety or reducing damage. Factors such as ductility and damping govern the structural response due to lateral loads. Despite the significant contribution of damping in resisting lateral loads, especially at resonance, there is no accurate mathematical representation for it. The main objective of this study is to develop a damping identification procedure for linear systems based on a mixed numerical-experimental approach, assuming viscous damping. The proposed procedure has been applied to a laboratory experiment associated with a numerical model, where a hollow rectangular steel cantilever column, having three lumped masses, has been fixed on a shaking table subjected to different exciting waves. The modal damping ratio has been identified; in addition, the effect of adding filling material to the hollow specimen has been studied in relation to damping enhancement. The results have revealed that the numerically computed response based on the identified damping is in a good fitting with the measured response. Moreover, the filling material has a significant effect in increasing the modal damping.