• Smart Structures and Systems
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• v.24 no.2
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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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.1 s.53
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• pp.205-213
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• 2009

This study was planned to develop monitoring system of cable force to resolve cable force of cable-stayed bridge efficiently in a long-term plan. In the proposed monitoring system, firstly data are sampled from real-time acceleration record, secondly these sampled data are frequency analyzed by using the FFT(Fast Fourier Transform) algorism and lastly the analyzed results are averaged and generalized. For evaluating the applicability of this monitoring system, field loading test has performed in real cable-stayed bridge. In comparison with cable force by field manual calculation and cable force of monitoring system by semi-automatic calculation, the difference of calculated cable forces has within 1% error range and it is acceptable range. Additionally within negligible 5% error range of difference has occur between field manual calculation and monitoring system by automatic calculation. so monitoring system in this study has been verified to be reliable.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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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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• v.16 no.1
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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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• v.71 no.2
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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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• v.12 no.2
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.1-8
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• 2009

The proposed real-time structural health monitoring(SHM) system in past transferred and received data, central server gathered data from sensors, through coaxial cable. an immense sum of money is required to structure sensor network using coaxial cable. This paper proposes USN-based structural health monitoring(SHM). AIso, this paper designs and realizes prototypes according to proposed SHM. The value of sensing data obtained through HSDPA transfer to the BMS(Bridge Monitoring Server) passing through the TCP / IP socket by building two-way communication system, We have implemented a complete graph converting full system.

• Smart Structures and Systems
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• v.18 no.3
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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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• v.23 no.3
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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.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.461-480
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• 2010

This paper analyses the data collected from the $2^{nd}$ Jindo Bridge, a cable-stayed bridge in Korea that is a structural health monitoring (SHM) international test bed for advanced wireless smart sensors network (WSSN) technology. The SHM system consists of a total of 70 wireless smart sensor nodes deployed underneath of the deck, on the pylons, and on the cables to capture the vibration of the bridge excited by traffic and environmental loadings. Analysis of the data is performed in both the time and frequency domains. Modal properties of the bridge are identified using the frequency domain decomposition and the stochastic subspace identification methods based on the output-only measurements, and the results are compared with those obtained from a detailed finite element model. Tension forces for the 10 instrumented stay cables are also estimated from the ambient acceleration data and compared both with those from the initial design and with those obtained during two previous regular inspections. The results of the data analyses demonstrate that the WSSN-based SHM system performs effectively for this cable-stayed bridge, giving direct access to the physical status of the bridge.