• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.357-361
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• 2011

본 논문에서는 확률적 불확실성을 포함한 손상 장에서 강성저감 효과를 추정하는 방법을 제안하였다. 실제 교량 구조물에 분포된 손상 장은 매우 불확실하며 손상의 위치와 형상 또한 정확히 알 수 없는 경우가 많다. 그러나 대부분의 손상 추정 문제는 균열이나 손상의 위치와 형상을 기지의 주어진 정보로 가정하고 손상을 추정한다. 제안 기법에서는 이러한 손상의 위치와 형태가 본질적으로 불확실하다는 가정 하에 이 불확실성을 수정 가우스 강성 저감 분포 함수를 도입하여 기술한다. 교량에 국부적으로 발생된 손상은 교량의 요소강성의 저감 분포로 변환되어 손상이 발생한 전체 시스템의 강성을 표현하고 이를 통해 손상이 발생한 시스템의 전체 응답을 해석할 수 있게 된다. 수정 가우스 강성 저감 분포 함수는 손상 분포의 개략적 중심을 표현하는 평균 변수와 강성 저감의 비국소적 분포 특성을 묘사하는 표준편차 변수, 손상 중심의 손상 정도를 표현하는 강성저감 변수로 구성된다. 본 논문에서는 손상 장에서 손상의 위치나 형태에 대한 확률적 불확실성을 기술하는 수정 가우스 강성 저감 분포 함수를 포함한 유한요소모델을 정식화하여 제시한다. 또한 단일 또는 복합 균열로 인해 교량 구조물에 국부적인 손상이 야기된 경우에 대한 수치 예제를 통하여 균열 등에 대한 정보가 불확실하더라도 수정 가우스 강성 저감 분포 함수를 통해 강성 저감 효과가 분석될 수 있음을 확인하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.296-299
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• 2009

본 논문은 차분진화 알고리즘을 이용한 다중 손상된 RC 슬라브 교량에 대한 시스템 인식(System Identification)기법을 소개한다. 제안된 기법을 이용하여 이동하중에 의한 교량의 동적응답을 기반으로 손상유무, 위치, 크기가 추정된다. ABAQUS를 이용한 손상된 3차원 슬라브 모델을 실험대상으로 하여, 모델로부터 동적응답을 찾아내었다. 차분진화 알고리즘(Differential Evolutioinary algorithm)을 기반으로 동적응답과 Bi-variate Gaussian 함수로 강성저하된 2차원 유한요소 MZC모델을 이용하여 손상된 위치와 크기, 이동하중의 크기와 속도가 추정되었다. 차분진화 알고리즘을 이용한 RC교량의 손상위치와 이동하중에 대한 추정은 3%이내의 오차를 보였고, 이로부터 제안된 방법의 효율성과 정확성이 검증되었다.

• 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 the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.769-777
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• 2015

In this paper, a damage assessment technique using a moving load test and optical sensors was studied to overcome the deficiency of measurement information in bridge maintenance. Continuous displacements by applying the reciprocal theorem to the test can make the assessment simpler and more practical. Numerical and experimental studies were performed to show the efficiency and accuracy of the proposed technique as well as the possibility of a more realistic assessment for large infrastructure. The results showed that the assessed damage levels are quite accurate, and similar to the exact values in actual damage locations, even in the experiments. The proposed technique is useful and practical for both detecting damage locations and damage quantities.

• Journal of Korean Society of Steel Construction
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• v.10 no.2 s.35
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• pp.279-290
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• 1998

An existing Damage Index Method is verified to demonstrate its feasibility for detecting structural damage in truss bridges (1) for which modal parameters are available for a few modes of vibration and (2) for which baseline modal information is not available from its as-built state. The theory of approach to detect locations of damage and to identify baseline modal model is summarized on the basis of system identification theory and modal sensitivity theory. The feasibility of the Damage Index Method is demonstrated using a numerical example of a truss bridge with 11 subsystems of 211 members and for which only two modes of vibration were recorded for post-damaged state.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.5
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• pp.13-22
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• 2019

In order to evaluate the earthquake damage level of small and medium - sized bridges without earthquake monitoring system, we proposed an algorithm for estimating the seismic force at the target bridge location using the ground acceleration data from the earthquake observatories near the structure. In general, response spectrum analysis, which is the most widely used dynamic analysis method to design and evaluate the structural system numerically is required a response spectrum to determine the dynamic loading. In this study, selection methods of the three closest observatories from the target structure and estimation method of ground response spectrum at arbitrary locations are developed. The proposed method can consider the distance and phase between the target bridge and the seismic station and from the relationship between the acceleration amplitudes and the location of the selected seismic station, the earthquake loading of the target bridge can be determined. The proposed algorithm is estimated to be more conservative than the response spectrum evaluated by actual earthquake data.

• Journal of KIBIM
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• v.10 no.2
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• pp.21-28
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• 2020

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.