• Smart Structures and Systems
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• 제17권6호
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• pp.995-1015
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• 2016

In the design and condition assessment of bridges, it is usually necessary to take into consideration the extreme conditions which are not expected to occur within a short time period and thus require an extrapolation from observations of limited duration. Long-term structural health monitoring (SHM) provides a rich database to evaluate the extreme conditions. This paper focuses on the extrapolation of extreme traffic load effects on bridges using long-term monitoring data of structural strain. The suspension Tsing Ma Bridge (TMB), which carries both highway and railway traffic and is instrumented with a long-term SHM system, is taken as a testbed for the present study. Two popular extreme value extrapolation methods: the block maxima approach and the peaks-over-threshold approach, are employed to extrapolate the extreme stresses induced by highway traffic and railway traffic, respectively. Characteristic values of the extreme stresses with a return period of 120 years (the design life of the bridge) obtained by the two methods are compared. It is found that the extrapolated extreme stresses are robust to the extrapolation technique. It may owe to the richness and good quality of the long-term strain data acquired. These characteristic extremes are also compared with the design values and found to be much smaller than the design values, indicating conservative design values of traffic loading and a safe traffic-loading condition of the bridge. The results of this study can be used as a reference for the design and condition assessment of similar bridges carrying heavy traffic, analogous to the TMB.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1996년도 가을 학술발표회 논문집
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• pp.131-137
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• 1996

To perform condition monitoring of P.C. Box girder bridge under ambient traffic, dynamic characteristics were identified using the results of load test an analysis. It was found that natural frequencies obtained from the measured acceleration data for the forced vibration part and free vibration part were nearly identical. Thus it can be concluded that dynamic parameters are properly determined under ambient traffic condition. Finite element model for analysis was calibrated using measured frequencies. Change of dynamic characteristics were predicted through analysis of the established finite element model with anticipated change.

• Smart Structures and Systems
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• 제5권4호
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• pp.381-395
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• 2009

The paper presents a case study in which the structural condition assessment of the East Bay bridge in Gibsonton, Florida is evaluated with the help of remote health monitoring techniques. The bridge is a four-span, continuous, deck-type reinforced concrete structure supported on prestressed pile bents, and is instrumented with smart Fiber Optic Sensors. The sensors used for remote health monitoring are the newly emerged Fabry-Perot (FP), and are both surface-mounted and embedded in the deck. The sensing system can be accessed remotely through fast Digital Subscriber Lines (DSL), which permits the evaluation of the bridge behavior under live traffic loads. The bridge was open to traffic since March 2005, and the collected structural data have been continuously analyzed since. The data revealed an increase in strain readings, which suggests a progression in damage. Recent visual observations also indicated the presence of longitudinal cracks along the bridge length. After the formation of these cracks, the sensors readings were analyzed and used to extrapolate the values of the maximum stresses at the crack location. The data obtained were also compared to initial design values of the bridge under factored gravity and live loads. The study showed that the proposed structural health monitoring technique proved to provide an efficient mean for condition assessment of bridge structures providing it is implemented and analyzed with care.

• 한국ITS학회 논문지
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• 제10권6호
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• pp.40-52
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• 2011

최근 지능형교통체계(ITS)는 다기능 검지기, 도시교통정보시스템, 단거리 전용 통신 등 첨단 검지장비가 도입되면서 교통정보의 신뢰성이 중요하게 대두되고 있다. 이 연구는 구간교통정보 산정과 같은 정량적 연구와 다르게 교통류 안정성을 반영한 정성적 소통상황 판단 알고리즘을 개발하였다. 이를 위해서 제한속도별 정성적 소통상황 분류기준을 재정립하고, 실시간 소통상황 판단 유형과 판단지표를 새롭게 제시하였다. 실시간 소통상황 판단 유형은 정성적 소통상황에서 속도추이의 상승, 진동, 하락에 따라 크게 9가지 유형으로 세분화하였다. 소통상황 판단지표는 속도변화추이를 파악하기 위해 과거 5주기 2분 단위 속도, 가속도를 나타내는 값과 부호로 정의한 3개 지표와 시간대별, 속도대별 불안정범위를 설정한 영역으로 정의하였다. 이 연구에서 개발된 소통상황 판단 알고리즘의 성능평가는 실제 검지자료를 이용하여 현장 적용성을 검증하였으며, 교통정보가공체계, 상황판 운영모니터링, 과거이력자료 활용 등에 적용이 가능할 것으로 판단된다.

• 대한교통학회지
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• 제27권5호
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• pp.85-95
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• 2009

지속적인 도로건설에도 불구하고 이용차량의 급증으로 인해 고속도로의 혼잡은 계속 커져 가고 있다. 이를 해결하기 위한 물리적 용량 확대 즉, 도로의 신설, 확장에는 막대한 비용뿐만 아니라 용지확보의 어려움 등으로 장기의 건설기간이 소요되어 혼잡 해소 시까지 상당기간 동안 혼잡의 지속이 불가피하다. 따라서 도로 관리주체는 소통상태가 악화되기 이전에 소통상태에 대한 지속적인 모니터링을 통해 혼잡원인 분석 및 해결대안을 제시하여 적극적으로 혼잡에 대처하는 것이 요구된다. 소통상태의 모니터링을 위해서는 소통상태를 계량적으로 표현할 수 있는 지표가 필요하다. 따라서 본 연구에서는 등급(또는 설계속도)이 다른 도로에 대해 동일한 척도로 평가가 가능하고 소통원활, 혼잡 등의 상태를 구분할 수 있는 혼잡지표(종합소통지수, TCI)를 개발하였다. 또한 개발된 종합소통지수(TCI)는 고속도로 교통관리시스템(FTMS)과 같이 기 설치된 시스템으로부터 획득 가능한 교통데이터를 이용하여 혼잡도를 산출할 수 있기 때문에 기존의 FTMS를 기능적으로 보완할 수 있고 도로 관리주체가 혼잡관리에 쉽게 적용할 수 있는 장점을 지닌다. 한편, TCI의 적용성을 평가하기 위해 경부 및 서해안 고속도로에 적용하여 평균통행속도, TTI 및 TCI 결과 값을 비교하였는데, 활용도, 소통상태 표현 능력 등의 측면에서 TCI의 적용성이 우수한 것으로 나타났다.

• 중소기업융합학회논문지
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• 제5권4호
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• pp.5-10
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• 2015

ICT 발달로 인해 네트워크의 상태를 파악하고, 분석하는 네트워크 모니터링이 중요한 이슈가 되었다. TCP/IP 네트워크 상에서 SNMP는 폴링기법을 이용하여 네트워크 상태를 파악하는 대표적인 프로토콜이다. 폴링기법을 긴 주기로 시행했을 때 네트워크 상태 변화를 제대로 파악하기 어렵다. 반면에 그 주기가 짧으면 실시간으로 네트워크 상태를 파악할 수 있겠지만 폴링결과에 대한 응답메시지로 인해 트래픽이 증가하여 네트워크에 부담이 된다. 폴링 주기를 조절하므로 폴링 응답메시지에 대한 오버헤드를 조절하기 위한 논문들이 제안되었다. 하지만, 에이전트의 특성을 고려하지 않고, 랜덤하게 실시하여 일시적인 효과일 뿐 효율적으로 오버헤드를 줄이지 못한다. 이 논문에서는 폴링 트래픽의 오버헤드를 줄이면서 네트워크 상태를 실시간으로 파악할 수 있는 효율적인 폴링 기법을 제안한다. 제안 기법은 폴링주기를 짧게 실시하면서 네트워크 상태를 실시간으로 파악하기 위해 에이전트의 특성에 따라 가중치를 부여하고, 가중치에 따라 매니저가 차등적으로 폴링을 실시여부를 결정하여 폴링 트래픽에 대한 오버헤드를 줄였다.

• Smart Structures and Systems
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• 제24권6호
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• 한국도로학회논문집
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• 제10권3호
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• pp.119-128
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• 2008

지속적인 도로 건설에도 불구하고 이용차량의 급증으로 인해 도로에서 발생하는 혼잡은 만성적인 수준에 도달했으며, 도로에서의 이동성 및 정시성 저하를 초래하는 등 심각한 교통문제를 유발하고 있다. 이러한 교통문제를 해결하기 위해서는 소통상태 추이 및 혼잡 징후 파악, 혼잡 원인 및 영향범위 분석 등을 통해 혼잡관리 방안을 수립 시행하는 일련의 과정이 필요한데, 이를 위해서는 교통상태를 모니터링 할 수 있는 지표의 개발이 가장 선행되어야 할 중요한 사항이다. 이러한 이유로 미국을 비롯한 여러 나라들에서는 다양한 형태의 혼잡판단기준 및 혼잡지표를 개발하여 왔다. 본 연구에서는 국내 실정에 부합하고 실효성 있는 교통소통관리를 위해 기존 혼잡지표들의 특징 및 국내 적용 가능성을 검토하여 향후 새로운 혼잡지표의 기능 및 요구사항 등에 대한 개발방향을 제시하였다.

• Smart Structures and Systems
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• 제24권5호
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Smart Structures and Systems
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• 제20권2호
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• pp.139-150
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• 2017

The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with long-term SHM system.