• Journal of the Computational Structural Engineering Institute of Korea
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• v.14 no.1
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• pp.77-85
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• 2001

본 연구에서는 차량하중에 의한 상시진동기록을 이용한 교량의 손상추정기법을 연구하였다. 즉, 차량진행 중 측정된 신호로부터 구조물의 모드특성을 구하고, 이를 이용하여 손상위치 및 손상정도를 추정하는 알고리즘을 제안하였다. 제안기법의 검증을 위하여 차량하중을 재하할 수 있는 모형교량을 제작하여 손상실험을 수행하였다. 차량진행 중 교량의 수직가속도를 계측하였으며, 측정된 가속도시계열로부터 random decrement(RD) 기법을 사용하여 자유진동신호를 구한 후, 이로부터 구조물의 모드특성을 추정하였다. 추정된 모드특성을 기초로 신경망기법을 적용하여 손상위치 및 손상정도를 추정하였으며, 추정된 결과는 실제 손상과 비교적 잘 일치하였다.

• Journal of Korean Society of Steel Construction
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• v.15 no.6 s.67
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• pp.603-610
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• 2003

A damage identification technique using static displacement data is developed to asses s the structural integrity of bridge structures.As such, the relationship between static displacement and stiffness is derived, and the optimization technique utilized.Comparisons with numerical and experimental tests are performed to investigate the practical applicability of the proposed method.Various damage scenarios are considered by varying damage-width as well as damage-degree. The influence of noise in identifying the damage is also numerically investigated.Finally, the applicability and limitation of the proposed method are discussed.

• Journal of Korean Society of Steel Construction
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• v.14 no.5 s.60
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• pp.641-646
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• 2002

A new damage detection technique using static displacement data was developed, in order to assess the structural integrity of bridge structures. In conventional damage assessment techniques using dynamic response, the variation of natural frequencies is intrinsically insensitive to the damage of the bridge: thus, it is usually difficult to obtain them from the measured data. The proposed detection method enables the estimation of the stiffness reduction of bridges using the static displacement data that are measured periodically, without requiring a specific loading test. Devices such as a laser displacement sensor can be used to measure static displacement data due to the dead load of the bridge structure. In this study, structural damage was represented by the reduction in the elastic modulus of the element. The damage factor of the element was introduced to estimate the stiffness reduction of the bridge under consideration. Likewise, the proposed algorithm was verified using various numerical simulations and compared with other damage detection methods. The effects of noise and number of damaged elements on damage detection were also investigated. Results showed that the proposed algorithm efficiently detects damage on the bridge.

• Journal of the Earthquake Engineering Society of Korea
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• v.2 no.1
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• pp.35-46
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• 1998

Structural damage is used to be modeled through reductions in the stiffness of structural elements for the purpose of damage estimation of structural system. In this study, the concept of joint damage is employed for more realistic damage assessment of a steel structure. The joint damage is estimated damage based on the mode shape informations using neural networks, The beam-to-column connection in a steel frame structure is represented by a rotational spring at the fixed end of a beam element. The severity of joint damage is defined as the reduction ratio of the connection stiffness with respect to the value of the intact joint. The concept of the substructural identification is used for the localized damage assessment in a large structure. The feasibility of the proposed method is examined using an example with simulated data. It has been found that the joint damages can be reasonably estimated for the case with the measurements of the mode vectors subjected to noise.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.87-94
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• 2012

A damage estimation method of wind turbine tower using natural frequency and mode shape is presented for effective condition monitoring. Dynamic analysis for a wind turbine was carried out to obtain the response of tower from which modal properties were identified. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. The changes of modal property were calculated using a program for modal parameter estimation. Damage locations and severities could be successfully estimated for 10 damage cases including multi-damage cases using the trained neural network. The damage severities for very small damages generally tends to be slightly under-estimated however, the identified damage locations agreed reasonably well with the accurate locations. Enhancement of the estimation result for very small damage and verification of the proposed method through experiment will be carried out by further study.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.1-11
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• 2008

Stiffness estimation of a shear building due to local damages is usually achieved though structural analysis based on the assumed material properties and idealized numerical modeling of structure. Conventional numerical modeling, however, frequently causes an inevitable error in the structural response and this makes it difficult to exactly predict the damage state in structure. To solve this problem, this paper introduces a damage detection technique for shear building using genetic algorithm. The introduced algorithm evaluates the damage in structure using a flexibility matrix since the flexibility matrix can exactly be obtained from the field test in spite of using a few lower dynamic modes of structure. The introduced algorithm is expected to be more effectively used in damage detection of structures rather than conventional method using the stiffness matrix. Moreover, even in cases when an accurate measurement of structural stiffness cannot be expected, the proposed technique makes it possible to estimate the absolute change in stiffness of the structure on the basis of genetic algorithm. The validity of the proposed technique is demonstrated though numerical analysis using OPENSEES.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.4
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• pp.35-42
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• 1990

A method for the damage assessment of a structure by an inverse modal perturbation technique is studied. The first few natural frequencies and mode shapes of the damaged structure are assumed to be known. Then, the perturbation equation is formulated for the changes of the modal properties due to the stiffness changes. The stiffness changes due to damages are evaluated, using optimization techniques. Example analyses are carried out for several cases of stick models and a truss model. Results indicate that the present method yields very reasonable estimates for the element stiffness changes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.64-71
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• 2017

A damage estimation method is presented for jacket-type support structure of offshore wind turbine using a change of modal properties due to damage and committee of neural networks for effective structural health monitoring. For more practical monitoring, it is necessary to monitor the critical and prospective damaged members with a limited number of measurement locations. That is, many data channels and sensors are needed to identify all the members appropriately because the jacket-type support structure has many members. This is inappropriate considering economical and practical health monitoring. Therefore, intensive damage estimation for the critical members using a limited number of the measurement locations is carried out in this study. An analytical model for a jacket-type support structure which can be applied for a 5 MW offshore wind turbine is established, and a training pattern is generated using the numerical simulations. Twenty damage cases are estimated using the proposed method. The identified damage locations and severities agree reasonably well with the exact values and the accuracy of the estimation can be improved by applying the committee of neural networks. A verification experiment is carried out, and the damage arising in 3 damage cases is reasonably identified.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.125-138
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• 2006

The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in data acquisition system andinformation processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, damage detection of bridge structures using neural networks technique based on the modal properties is presented, which can effectively consider the modeling uncertainty in the analysis model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness and applicability of the proposed method.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.5
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• pp.369-376
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• 2014

This paper presents a method that integrates passive and active-sensing techniques for the structural health monitoring of plate-like structures. Three piezoelectric transducers are deployed in a L-shape to detect and locate an impact event by measuring and processing the acoustic emission data. The same sensor arrays are used to estimate the subsequent structural damage using guided waves. Because this method does not require a prior knowledge of the structural parameters, such as the wave velocity profile in various directions, accurate results could be achieved even on anisotropic or curved plates. A series of experiments was performed on plates, including a spar-wing structure, to demonstrate the capability of the proposed method. The performance was also compared to that of traditional approaches and the superior capability of the proposed method was experimentally demonstrated.