Influence of Stationary Vehicles on Bridge Aerodynamic and Aeroelastic Coefficients

Pospı́šil, Stanislav; Buljac, Andrija; Kozmar, Hrvoje; Kuznetsov, Sergey; Macháček, Michael; Král, Radomil

doi:10.1061/(asce)be.1943-5592.0001017

Cited by 12 publications

(2 citation statements)

References 31 publications

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“…Based on previous studies, effects of excitation are researched, including seismic excitation, wind excitation, rain excitation, roughness excitation, and traffic flow excitation. e wind excitation is most widely concerned [24][25][26][27][28][29][30][31][32][33]. Dynamic analysis of the vehicle-bridge system is maturing, with a wealth of well-understood methods and algorithms, particularly in the wind-vehicle-bridge system.…”

Section: Introductionmentioning

confidence: 99%

Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long‐Span Bridge with Fully Connected Neural Network

Yang

et al. 2019

Advances in Civil Engineering

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The safety condition of vehicles passing on long-span bridges has attracted more and more attention in recent years. Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on fully connected neural network (FCN). The first step is to investigate the long-span bridge responses with wind excitation by using the wind tunnel test and finite element model. Subsequently, typical vehicle models are given and a vehicle-bridge system is established by considering weather conditions. Accident types of vehicles with severe weather are estimated. In particular, the input and output variables of the vehicle safety evaluation model are determined, and simultaneously training, validation, and testing data are achieved. Twenty-nine models have been compared and analyzed by using hidden layer, initial learning rate, batch size, activation function, and optimization method. It is found that the 4-15-15-4 model occupies a preferable prediction performance, and it can provide a kind of utility for traffic control and reduce the probability of vehicle accidents on the bridge.

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Section: Introductionmentioning

confidence: 99%

Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long‐Span Bridge with Fully Connected Neural Network

Yang

et al. 2019

Advances in Civil Engineering

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“…During the tests, the iced cable model was fixed horizontally in a special frame at a level of 69 cm above the floor of the aerodynamic section, crosswise to the airflow. Two sides of the frame were equipped with the plexi-glass end-plates to ensure two-dimensional flow around the model [11]. The height and the length of the plates were both 140.5 cm, the width was 17 cm and their spacing was 47 cm.…”

Section: Experimental Investigations Of Strouhal Numbermentioning

confidence: 99%

Investigation of Strouhal number of ice-accreted bridge cables at moderate flow turbulence

et al. 2017

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Abstract. This paper is concerned with the investigation of the Strouhal number (St) for a stationary iced cable model of cable-supported bridges using wind tunnel tests with respect to three principal angles of wind attack. The investigations were carried out in the Climatic Wind Tunnel Laboratory of the Czech Academy of Sciences in Telč. The St values were determined at the flow turbulence intensity of the order of 12%, within the range of the Reynolds number from 2.4·10 4 to 12.9·10 4 , based on the dominant vortex shedding frequencies measured in the wake of the model. The obtained experimental St values of the iced cable of a cable-supported bridge will allow to determine the critical wind velocity as well as will be helpful for prediction of the cable response due to the vortex excitation phenomenon at moderate flow turbulence condition.

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Experimental Investigations on the Flutter Derivatives of the Pedestrian-Bridge Section Models

2020

KSCE J Civ Eng

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Influence of Stationary Vehicles on Bridge Aerodynamic and Aeroelastic Coefficients

Cited by 12 publications

References 31 publications

Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long‐Span Bridge with Fully Connected Neural Network

Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long‐Span Bridge with Fully Connected Neural Network

Investigation of Strouhal number of ice-accreted bridge cables at moderate flow turbulence

Experimental Investigations on the Flutter Derivatives of the Pedestrian-Bridge Section Models

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