scholarly journals Changes in modal parameters of a steel truss bridge due to artificial damage

2014 ◽  
pp. 3725-3732
Author(s):  
C Kim ◽  
K Chang ◽  
S Kitauchi ◽  
P McGetrick ◽  
K Hashimoto ◽  
...  
Keyword(s):  
Modal Parameters ◽  
Artificial Damage ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Truss Bridge

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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