Fatigue Stress Analysis of a Railway Steel Truss Bridge Using Dynamic Vehicle-bridge System Model

2016 ◽  
Author(s):  
H.L. Li ◽  
H. Xia
Keyword(s):  
Stress Analysis ◽  
System Model ◽  
Fatigue Stress ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Truss Bridge ◽  
Bridge System

Vehicle Parameters Influence on Vertical Load-Carrying Natural Frequency of Railway Double-Track Steel Truss Continuous Bridge

2012 ◽  
Vol 446-449 ◽  
pp. 2613-2616
Author(s):  
Jian Ying Ren ◽  
Mu Biao Su ◽  
Wen Ping Li
Keyword(s):  
Natural Frequency ◽  
High Speed ◽  
Vertical Load ◽  
Suspension Spring ◽  
Steel Truss Bridge ◽  
Load Carrying ◽  
Steel Truss ◽  
Truss Bridge ◽  
Double Track ◽  
Bridge System

The railway double-track continuous bridge vertical load-carrying frequency is calculated with the vehicle-bridge system mode1. When two lines with 20 same high-speed passenger vehicles are traveling on the 3×64 m through stud welding railway steel truss bridge from both ends, it is analyzed that the vehicle parameters how to influence the bridge vertical load-carrying frequency. The bridge vertical load-carrying frequency is influenced by the unsprung mass of each wheel-set of the vehicle, the suspension spring stiffness, the vehicle length and the mass of the vehicle. But it is independent of the speed of the train．It is shown that the bridge vertical load-carrying frequency is the vehicle-bridge system natural frequency, it is only depend on its natural parameters.

Deconstruction Monitoring of a Steel Truss Bridge

2017 ◽  
Vol 2642 (1) ◽  
pp. 139-146
Author(s):  
Matthew Yarnold ◽  
Stephen Salaman ◽  
Eric James
Keyword(s):  
Steel Truss Bridge ◽  
Steel Truss ◽  
Truss Bridge

scholarly journals A Bayesian Belief Network method for bridge deterioration detection

2020 ◽  
pp. 1748006X2097922
Author(s):  
Matteo Vagnoli ◽  
Rasa Remenyte-Prescott ◽  
John Andrews
Keyword(s):  
Element Model ◽  
Bayesian Belief Network ◽  
Geographic Area ◽  
Detection Methods ◽  
Health State ◽  
Belief Network ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Network Method ◽  
Truss Bridge

Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge.

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