scholarly journals Load effect of multi-lane traffic simulations on long-span bridges

2012 ◽  
pp. 1460-1467
Author(s):  
C Caprani ◽  
A Lipari ◽  
E OBrien
Keyword(s):  
Load Effect ◽  
Long Span Bridges ◽  
Long Span ◽  
Traffic Simulations

Vehicle Load Effect of Long-Span Bridges

2015 ◽  
Vol 2481 (1) ◽  
pp. 132-139 ◽  
Author(s):  
Jun Wu ◽  
Fei Yang ◽  
Wanshui Han ◽  
Liujie Wu ◽  
Qiang Xiao ◽  
...  
Keyword(s):  
Load Effect ◽  
Long Span Bridges ◽  
Long Span ◽  
Vehicle Load

Alternate Designs for Long Span Bridges

PCI Journal ◽  
1980 ◽  
Vol 25 (4) ◽  
pp. 48-58
Author(s):  
Felix Kulka
Keyword(s):  
Long Span Bridges ◽  
Long Span

Wind Buffeting in Time Domain Analysis of Long-Span Bridges using RM Bridge

2017 ◽  
Vol 109 (6) ◽  
pp. 3307-3317
Author(s):  
Afshin Hatami ◽  
Rakesh Pathak ◽  
Shri Bhide
Keyword(s):  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Long Span Bridges ◽  
Long Span

scholarly journals Wind-Induced Phenomena in Long-Span Cable-Supported Bridges: A Comparative Review of Wind Tunnel Tests and Computational Fluid Dynamics Modelling

2021 ◽  
Vol 11 (4) ◽  
pp. 1642
Author(s):  
Yuxiang Zhang ◽  
Philip Cardiff ◽  
Jennifer Keenahan
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Tunnel ◽  
Careful Analysis ◽  
Wind Effects ◽  
Wind Tunnel Tests ◽  
Long Span Bridges ◽  
Long Span ◽  
Comparative Review ◽  
Dynamics Modelling

Engineers, architects, planners and designers must carefully consider the effects of wind in their work. Due to their slender and flexible nature, long-span bridges can often experience vibrations due to the wind, and so the careful analysis of wind effects is paramount. Traditionally, wind tunnel tests have been the preferred method of conducting bridge wind analysis. In recent times, owing to improved computational power, computational fluid dynamics simulations are coming to the fore as viable means of analysing wind effects on bridges. The focus of this paper is on long-span cable-supported bridges. Wind issues in long-span cable-supported bridges can include flutter, vortex-induced vibrations and rain–wind-induced vibrations. This paper presents a state-of-the-art review of research on the use of wind tunnel tests and computational fluid dynamics modelling of these wind issues on long-span bridges.

scholarly journals Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 180
Author(s):  
Lei Fu ◽  
Qizhi Tang ◽  
Peng Gao ◽  
Jingzhou Xin ◽  
Jianting Zhou
Keyword(s):  
Short Term Memory ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Damage Localization ◽  
Short Term ◽  
Term Memory ◽  
Localization Accuracy ◽  
Long Span Bridges ◽  
Long Span ◽  
Long Short Term Memory

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

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