Theory and case study of vehicle load identification based on BWIM of steel truss bridge

Stahlbau ◽  
2013 ◽  
Vol 82 (3) ◽  
pp. 214-222
Author(s):  
Weizhen Chen ◽  
Guang Yang
Keyword(s):  
Load Identification ◽  
Steel Truss Bridge ◽  
Vehicle Load ◽  
Steel Truss ◽  
Truss Bridge

Investigation of Temperature Effects on Steel-Truss Bridge Based on Long-Term Monitoring Data: Case Study

2020 ◽  
Vol 25 (9) ◽  
pp. 05020007
Author(s):  
Qing-Xin Zhu ◽  
Hao Wang ◽  
Jian-Xiao Mao ◽  
Hua-Ping Wan ◽  
Wen-Zhi Zheng ◽  
...  
Keyword(s):  
Temperature Effects ◽  
Monitoring Data ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Long Term Monitoring ◽  
Truss Bridge ◽  
Term Monitoring

scholarly journals Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1271 ◽  
Author(s):  
Asma Alsadat Mousavi ◽  
Chunwei Zhang ◽  
Sami F. Masri ◽  
Gholamreza Gholipour
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Steel Truss Bridge ◽  
Damage Indices ◽  
Bridge Model ◽  
Steel Truss ◽  
Truss Bridge

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.

scholarly journals Light Live load Bridges over the River Nile in Sudan

2021 ◽  
Vol 9 (1) ◽  
pp. 65-71
Author(s):  
Ahmed Gasim M. Hussein ◽  
Khalil Fawzi Ajabani
Keyword(s):  
Highway Bridges ◽  
Live Load ◽  
River Nile ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Bridge Structures ◽  
Live Loads ◽  
Truss Bridge ◽  
The Cost

Bridge structures are vital for majority of Sudanese due to the fact that they live besides rivers, valleys and inside islands. Bridge construction is faced by the fact that it is extremely expensive. Cost of such structures is affected by live load which accordingly dictates the required dead Loads from both superstructure and substructure. In this analytical study a light live bridge load is derived making use of AASHTO principles. This practical live load is derived from data collected from sedan cars, bicycles, motorcycles, motorcycles rickshaws, auto rickshaws and pedestrian. The derivation yielded a design light live load composed of design lane load and design vehicle; to be applied simultaneously to this type of light bridges. The live loads are to be controlled at the bridge entrance. The derived loads are applied to different superstructures' systems, namely steel truss and composite steel plate girder. A single pier over two piles substructure system is chosen for such light loads. A case study bridge is designed over the River Nile. The results obtained showed tremendous savings in material and cost. Relative to normal highway bridges over the Nile, the steel truss bridge option reduces the cost by almost 60%.  

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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