Stochastic Analysis of Long-Span Bridges with Active Control

1987 ◽  
pp. 136-164
Author(s):  
P. Ghaemmaghami ◽  
J. N. Yang
Keyword(s):  
Active Control ◽  
Stochastic Analysis ◽  
Long Span Bridges ◽  
Long Span

Study on Cable-Stayed Bridge Flutter Active Control by a Single ADM

2011 ◽  
Vol 383-390 ◽  
pp. 5071-5075
Author(s):  
Su Qi ◽  
Xing Xing Chen ◽  
Qing Xu
Keyword(s):  
Wind Speed ◽  
Active Control ◽  
Active Mass ◽  
Vortex Induced Vibration ◽  
Long Span Bridges ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Excited Vibration ◽  
Active Mass Damper ◽  
Wind Induced Vibration

Wind-induced vibration of long span bridges mainly as flutter, buffeting and vortex induced vibration. Buffeting and vortex-induced vibration will not cause the devastating destruction of the bridge, while the chatter is the elastic system in the air of self-excited vibration, when the vibration system from the air flow in the absorption of energy and the energy is greater than the energy damping When consumed, they cause divergence of the self-excited aerodynamic flutter vibration. If the critical flutter wind speed is less than in the bridge office potential wind speed, the bridge flutter may occur caused devastating damage. According to modern control theory, a theoretical analysis is conducted on the active control of cable-stayed bridge flutter, it is established that the controlled equation of cable-stayed bridge controlled by a single active mass damper and the motion equation of a single AMD to determine the calculation method of the critical flutter velocity under the controlled status of the cable-stayed bridge. An example shows that a single ADM is a good means to prevent the flutter damage of long-span cable-stayed bridges.

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

Innovative Steel-UHPC Composite Bridge Girders for Long-span Bridges

2016 ◽  
Vol 106 (7) ◽  
pp. 614-622
Author(s):  
Xudong SHAO ◽  
Lu DENG
Keyword(s):  
Bridge Girders ◽  
Long Span Bridges ◽  
Long Span ◽  
Composite Bridge

Indian railway bridges – adoption of new bearing technology to support the progression towards long span bridges

2013 ◽  
Vol 101 (4) ◽  
pp. 1-8
Author(s):  
Santanu Majumdar ◽  
Chinmoy Ghosh ◽  
Gianni Moor
Keyword(s):  
Railway Bridges ◽  
Long Span Bridges ◽  
Long Span ◽  
Indian Railway

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.

Damage detection of long-span bridges using stress influence lines incorporated control charts

2014 ◽  
Vol 57 (9) ◽  
pp. 1689-1697 ◽  
Author(s):  
ZhiWei Chen ◽  
QinLin Cai ◽  
Ying Lei ◽  
SongYe Zhu
Keyword(s):  
Damage Detection ◽  
Control Charts ◽  
Long Span Bridges ◽  
Long Span ◽  
Stress Influence
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