• Structural Monitoring and Maintenance
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• 제11권2호
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• pp.149-171
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• 2024

Drive-by monitoring (also known as indirect monitoring or mobile sensing) of bridges has obvious advantages when compared to other approaches of Structural Health Monitoring. The underlying concept involves leveraging the coupling between the vertical vibrations of the bridge and those generated in the passing vehicle. In this scenario, the vehicle serves as both the initiator and recipient of the vibrations, which can provide information on the structural condition of the bridge. In the literature, a wide range of methods has been proposed, primarily focused on highway bridges. However, limited research has been published to assess the suitability of indirect methods for monitoring railway bridges, bounded to numerical studies based on theoretical simulations and, rarely, on experimental investigations. The aim of this work is to contribute to filling this gap and explore the feasibility of implementing drive-by monitoring for railway bridges using in-service vehicles and discuss its potential applicability, from theoretical and practical point of view, with illustration through real case studies from the Moroccan railway network.

• Smart Structures and Systems
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• 제24권2호
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• 한국구조물진단유지관리공학회 논문집
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• 제13권1호통권53호
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• pp.205-213
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• 2009

본 연구에서는 사장교의 케이블 장력을 장기적인 관점에서 보다 효과적으로 분석하고 모니터링할 수 있는 케이블 장력 모니터링 시스템을 개발하는 연구를 수행하였다. 제안된 모니터링 시스템에는 실시간 동적 데이터에서 일정 대상시간 동안의 가속도 데이터를 선정하고, 이를 고속 푸리에 변환 알고리즘에 적용하여 주파수 분석한 결과를 평균하여 일반화시키는 신호해석이론이 적용되었다. 제안된 케이블 장력의 모니터링 시스템의 적용성을 평가하기 위하여 실제 공용중인 사장교에 대한 현장재하시험을 실시하였다. 현장계측을 통한 수동으로 계산된 장력과 모니터링 시스템의 강제저장 기능을 통해 저장된 데이터를 수계산하여 반자동으로 계산된 장력을 비교한 결과, 장력차이가 1% 이내로 발생하였고, 제안된 모니터링 시스템에서 자동으로 계산된 장력을 비교한 결과에서는 약 5% 범위의 무시할만한 차이로 나타나 본 연구에서 제안된 사장교 케이블 장력 모니터링 시스템의 신뢰도를 검증하였다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.195-196
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• 2010

This study aims to methodology a system which is able to monitoring and analysis of long span bridge in real time using multi GPS. Through setting up many GPS at the important points of long span bridge and measuring displacement in real time, over all 3D configuration of bridge could be analyzed. Behavior analyzing system developed in this study is able to digitize and visualize the overall and points displacement of bridge and deal with events actively. Also it is able to calculate statistical data related to analyze behavior through the constricting database of measuring data.

• Computers and Concrete
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• 제16권1호
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• pp.49-65
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• 2015

It is hard to guarantee the strict synchronization of all the jacking-up points in the integral jacking of a large-span continuous box girder bridge. This paper took the Hengliaojing Bridge as background, which need jacking up as an object with 295m length and more than 10,000tons weight, adopted 3D software to calculate the unsynchronized jacking-up working conditions, and studied the relationships between the unsynchronized vertical difference and the girder's deformation behaviour. The aim is to verify the maximum value of the unsynchronized vertical difference, and guide the construction and ensure safety. The monitoring system with its contents is introduced corresponding to the analysis. The results of the deck relative elevations prove that it is difficult to avoid the deck torsional deformation for jacking different; especially the side span shows more deformations for its smaller stiffness. The maximum difference is smaller than the limited value with acceptable stresses in the sections. The jacking heights of the pier in each construction step are controlled regularly according to the design. The shifting of the whole bridge in longitudinal direction is smaller than in transverse direction. The several beginning steps are the key to adjust their support reactions. This study is one parts of the fundamental research for the code "Technical specification for bridge jacking-up and reposition of China". The whole synchronous jacking project of the main bridge set a world record by the World Record Association for the whole bridge jacking project with the longest span of the world.

• Structural Engineering and Mechanics
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• 제71권2호
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• pp.175-183
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• 2019

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high.

• Smart Structures and Systems
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• 제12권2호
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• pp.181-207
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• 2013

In this paper, a statistical reference-free real-time damage detection methodology is proposed for detecting joint and member damage of truss bridge structures. For the statistical damage sensitive index (DSI), wavelet packet decomposition (WPD) in conjunction with the log likelihood ratio was suggested. A sensitivity test for selecting a wavelet packet that is most sensitive to damage level was conducted and determination of the level of decomposition was also described. Advantages of the proposed method for applications to real-time health monitoring systems were demonstrated by using the log likelihood ratios instead of likelihood ratios. A laboratory truss bridge structure instrumented with accelerometers and a shaker was used for experimental verification tests of the proposed methodology. The statistical reference-free real-time damage detection algorithm was successfully implemented and verified by detecting three damage types frequently observed in truss bridge structures - such as loss of bolts, loosening of bolts at multiple locations, sectional loss of members - without reference signals from pristine structure. The DSI based on WPD and the log likelihood ratio showed consistent and reliable results under different damage scenarios.

• 한국인터넷방송통신학회논문지
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• 제9권4호
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• pp.1-8
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• 2009

본 연구 이전에 제안되었던 실시간 교량 모니터링 시스템은 교량 곳곳에 배치되어 있는 센서들로부터 중앙서버로 동축 케이블을 통해 데이터를 송수신하였는데, 동축 케이블을 이용하여 교량 전체의 센서들의 네트워크를 구성하기 위해서는 막대한 인력과 비용이 따르게 된다. 본 연구에서는 USN을 기반으로 한 교량 모니터링 시스템 제안하고, 이에 대한 프로토타입을 설계 및 구현하였다. HSDPA를 통해 얻은 센싱 데이터 값을 TCP/IP 소켓을 통해 교량 모니터링 서버에 전달함으로써 양방향 통신을 구축하여 그래프 변환을 하는 전체 시스템을 구현하였다.

• Smart Structures and Systems
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• 제18권3호
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• pp.585-599
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• 2016

Steel cables serve as the key structural components in long-span bridges, and the force state of the steel cable is deemed to be one of the most important determinant factors representing the safety condition of bridge structures. The disadvantages of traditional cable force measurement methods have been envisaged and development of an effective alternative is still desired. In the last decade, the vision-based sensing technology has been rapidly developed and broadly applied in the field of structural health monitoring (SHM). With the aid of vision-based multi-point structural displacement measurement method, monitoring of the tensile force of the steel cable can be realized. In this paper, a novel cable force monitoring system integrated with a multi-point pattern matching algorithm is developed. The feasibility and accuracy of the developed vision-based force monitoring system has been validated by conducting the uniaxial tensile tests of steel bars, steel wire ropes, and parallel strand cables on a universal testing machine (UTM) as well as a series of moving loading experiments on a scale arch bridge model. The comparative study of the experimental outcomes indicates that the results obtained by the vision-based system are consistent with those measured by the traditional method for cable force measurement.

• Smart Structures and Systems
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• 제23권3호
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.