Concrete box girder bridge assessment – a stiffness adaptation approach

2015 ◽  
pp. 1127-1134
Keyword(s):  
Box Girder ◽  
Concrete Box Girder ◽  
Box Girder Bridge ◽  
Girder Bridge ◽  
Bridge Assessment

Optimization on Closure Scheme of Multi-Span Prestressed Concrete Box-Girder Bridge

2010 ◽  
Vol 163-167 ◽  
pp. 2369-2375 ◽  
Author(s):  
Ming Yuan ◽  
Dong Huang Yan
Keyword(s):  
Stress State ◽  
Prestressed Concrete ◽  
Box Girder ◽  
Rigid Frame ◽  
Closure Scheme ◽  
Concrete Box Girder ◽  
Box Girder Bridge ◽  
Girder Bridge ◽  
Cantilever Construction ◽  
Rigid Frame Bridge

The stress state of finished bridge and service stage is influenced by various closure schemes in cantilever construction of multi-span prestressed concrete box-girder bridge. Two typical bridges—multi-span prestressed concrete continuous rigid frame bridge and girder bridge are investigated, The stress state in different closure schemes are analyzed using finite element(FE) analysis. Meanwhile, compared the healthy monitoring data, it has been found that taking the closure sequence from side span to middle span in cantilever construction of multi-span prestressed concrete box-girder bridge can lower stress of girder and pier in finished bridge stage, as well as reducing deformation of girder in service stage. Hence, the closure sequence from side span to middle span is more suitable for cantilever construction of multi-span prestressed concrete box-girder bridge.

Live-load strain evaluation of the prestressed concrete box-girder bridge using deep learning and clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 1051-1063 ◽  
Author(s):  
Hanwei Zhao ◽  
Youliang Ding ◽  
Aiqun Li ◽  
Zhaozhao Ren ◽  
Kang Yang
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prestressed Concrete ◽  
Gaussian Mixture ◽  
Live Load ◽  
Box Girder ◽  
Concrete Box Girder ◽  
Box Girder Bridge ◽  
Girder Bridge

The monitoring data makes it feasible to quickly evaluate the cracking of the prestressed concrete box-girder bridge. The live-load strain can accurately quantify the load effect and cracking of bridges due to its explicit datum point of signal. Based on the live-load strain data from bridge monitoring system, this study develops a comprehensive data-driven method of state evaluation and cracking early warning for the prestressed concrete box-girder bridge. The feature of vehicle-induced strain is extracted using the deep learning and classification of long short-term memory network. The vehicle-induced strain features are clustered via Gaussian mixture model, and the cracking early warning of the bridge is conducted according to the reliability of heavy vehicle clustering data. This method can be used as an indicator for the bridge inspection, truck-weight-limit and reinforcement work. The results demonstrate that (1) using the long short-term memory network, a deep learning model can be trained to intelligently classify the non-stationary and stationary sections of vehicle-induced strains, of which the test accuracy of classification surpasses 99%, and (2) according to the Gaussian mixture model probability distribution of data, the vehicle-induced strain features can be clustered by the corresponding Gaussian mixture model crest, which is the premise for reflecting relational mapping between vehicle loading and strain response.

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