• Structural Engineering and Mechanics
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• 제77권5호
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• pp.627-635
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• 2021

The speed of rail vehicles become higher and higher over two decades, and China has unveiled a prototype high-speed train in October 2020 that has been able to reach 400 km/h. At such high speeds, wheel-rail force items that had previously been ignored in common computational model should be reevaluated and reconsidered. Aiming at this problem, a new model for investigating the vehicle-bridge interaction at high moving speed is proposed. Comparing with the common model, the new model was more accurate and applicable, because it additionally considers the second-order pseudo-inertia forces effect and its modeling equilibrium position was based on the initial deformed curve of bridge, which could include the influences of temperature, pre-camber, shrinkage and creep deformation, and pier uneven settlement, etc. Taking 5 km/h as the speed interval, the dynamic responses of the classical vehicle-bridge system in the speed range of 5 km/h to 400 km/h are studied. The results show that ignoring the second-order pseudo-inertia force will underestimate the dynamic response of vehicle-bridge system and make the high-speed railway bridge structure design unsafe.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.1176-1179
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• 2004

A new cell-cross bridge mechanics model is proposed to analyze the mechanics of heart muscle. Electrophysiology of a cardiac cell is numerically approximated using the previous model of human ventricular myocyte. Ion transports across cell membrane initiated by action potential induce excitation-contraction mechanism in the cell via cross bridge dynamics. Negroni and Lascano model (NL model) is employed to compute the tension of cross bridge closely related to ion dynamics in cytoplasm.

• Smart Structures and Systems
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• 제26권4호
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• pp.533-544
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• 2020

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 가을 학술발표회 논문집
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• pp.444-451
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• 2003

In this study, a new three-dimensional finite element analysis model of high-speed railway bridges considering train-bridge interaction, in which various improved finite elements are used for modeling structural members, is proposed. The box-type bridge deck of a railway bridge is modeled by the NFS(Nonconforming Flat Shell) elements with 6 degrees of freedom. Track structures are idealized using the beam finite elements with the offset of beam nodes and those on Winkler foundation with two parameters. And, the vehicle model devised for a high-speed train is employed, which has an articulated bogie system. By Lagrange's equations of motion, the equations of motion of a bridge-train system can be formulated. Finally, by deriving the equations of the forces acting on a bridge considering bridge-train interaction the complete system matrices of total bridge-train system can be constructed. As numerical examples of this study, 2-span PC box-girder bridge is analyzed and results are compared with experimental results.

• Earthquakes and Structures
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• 제16권4호
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• pp.391-399
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• 2019

Residual deformation of high-speed railway bridge piers is cumulative under repeated earthquakes, and influences the safety and ride comfort of high-speed trains. This paper investigates the effects of the peak ground acceleration, longitudinal reinforcement ratio, and axial compression ratio on the cumulative deformation through finite element analysis. A simply-supported beam bridge pier model is established using nonlinear beam-column elements in OpenSees, and validated against a shaking table test. Repeated earthquakes were input in the model. The results show that the cumulative deformation of the bridge piers under repeated earthquakes increases with the peak ground acceleration and the axial compression ratio, and decreases with the longitudinal reinforcement ratio.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.55-65
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• 2009

This study is objected by analyzing whether it is applied to the analysis model considering the track stiffness or not when the railway bridge is designed or reviewed for the dynamic stability. It is performed that the analysis model is verified by comparing the field test result with the analysis result. Also, The dynamic response of railway bridge through the existing analysis model is compared with the analysis model considered the track stiffness. In addition, it is performed by analyzing the model considering the stiffness of concrete track. Therefore, this study is suggested that the design of railway bridge apply to the existing analysis model considering the mass of track and the dynamic stability review of railway bridge apply to it considered the stiffness & mass of track. Also, it is suggested that the stiffness of concrete slab on the bridge must consider when it is designed or checked over the dynamic stability.

• Smart Structures and Systems
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• 제11권5호
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• pp.533-553
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• 2013

In this paper, system identification of a cable-stayed bridge in Korea, the Hwamyung Bridge, is performed using vibration responses measured by a wireless sensor system. First, an acceleration based-wireless sensor system is employed for the structural health monitoring of the bridge, and wireless sensor nodes are deployed on a deck, a pylon and several selected cables. Second, modal parameters of the bridge are obtained both from measured vibration responses and finite element (FE) analysis. Frequency domain decomposition and stochastic subspace identification methods are used to obtain the modal parameters from the measured vibration responses. The FE model of the bridge is established using commercial FE software package. Third, structural properties of the bridge are updated using a modal sensitivity-based method. The updating work improves the accuracy of the FE model so that structural behaviors of the bridge can be represented better using the updated FE model. Finally, cable forces of the selected cables are also identified and compared with both design and lift-off test values.

• Structural Engineering and Mechanics
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• 제59권4호
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• pp.703-718
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• 2016

Masonry arch bridges present a large segment of Iranian railway bridge stock. The ever increasing trend in traffic requires constant health monitoring of such structures to determine their load carrying capacity and life expectancy. In this respect, the performance of one of the oldest masonry arch bridges of Iranian railway network is assessed through field tests. Having a total of 11 sensors mounted on the bridge, dynamic tests are carried out on the bridge to study the response of bridge to test train, which is consist of two 6-axle locomotives and two 4-axle freight wagons. Finite element model of the bridge is developed and calibrated by comparing experimental and analytical mid-span deflection, and verified by comparing experimental and analytical natural frequencies. Analytical model is then used to assess the possibility of increasing the allowable axle load of the bridge to 25 tons. Fatigue life expectancy of the bridge is also assessed in permissible limit state. Results of F.E. model suggest an adequacy factor of 3.57 for an axle load of 25 tons. Remaining fatigue life of Veresk is also calculated and shown that a 0.2% decrease will be experienced, if the axle load is increased from 20 tons to 25 tons.

• Wind and Structures
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• 제4권6호
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• pp.505-518
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• 2001

Shantou Bay Bridge is the first long-span suspension bridge in China. Because of its location near the Shantou Seaport and its exposure to high typhoon winds, wind-resistant studies are necessary to be made. In this paper, critical flutter wind speeds and buffeting responses of this bridge at its operation and main construction stages are investigated. The Buffeting Response Spectrum method is first briefly presented. Then the sectional model test is carried out to directly obtain the critical flutter wind speed and to identify the flutter derivatives, which are adopted for the later analysis of the buffeting responses using the Buffeting Response Spectrum method. Finally the aeroelastic full bridge model is tested to further investigate the dynamic effects of the bridge. The results from the tests and the computations indicate that the flutter and buffeting behaviors of the Shantou Bay Bridge are satisfied.

• Smart Structures and Systems
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• 제28권6호
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.