Seismic performances of magnetorheological flag-shaped damping braced frame structures

2020 ◽  
Vol 29 (7) ◽  
pp. 075032
Author(s):  
Long-He Xu ◽  
Xing-Si Xie ◽  
Zhong-Xian Li
Keyword(s):  
Frame Structures ◽  
Braced Frame

scholarly journals Effect of Fling-Step on Seismic Response of Steel Eccentrically Braced Frame Structures

2018 ◽  
Vol 2 (3) ◽  
pp. 1-13
Author(s):  
B. Rezayibana ◽  
S. Norozikalehsar ◽  
B. Nourollahi ◽  
◽  
◽  
...  
Keyword(s):  
Seismic Response ◽  
Frame Structures ◽  
Braced Frame ◽  
Eccentrically Braced Frame ◽  
Fling Step

Seismic response of concentrically braced steel frames

1991 ◽  
Vol 18 (6) ◽  
pp. 1062-1077 ◽  
Author(s):  
Richard G. Redwood ◽  
Feng Lu ◽  
Gilles Bouchard ◽  
Patrick Paultre
Keyword(s):  
Structural Engineering ◽  
Nonlinear Response ◽  
Steel Structures ◽  
Frame Structures ◽  
Braced Frames ◽  
Braced Frame ◽  
Concentrically Braced ◽  
Engineering Steel ◽  
Analysis Design ◽  
Improved Design

Braced frame structures designed according to the 1990 edition of the National Building Code of Canada and the CSA standard for steel structures (CAN/CSA-S16.1-M89) are analyzed under a number of different earthquake motions. The nonlinear response is studied in the light of the design philosophy, and the validity of a number of design assumptions is examined. The study is limited to a group of eight-storey frames, located either in Victoria, British Columbia, or Montreal, Quebec, all with the same bracing configuration. A 20-storey frame in Montreal is also considered. The results suggest a number of areas in which improved design provisions could be made. Key words: analysis, design, structural engineering, steel, earthquakes, braced frames.

Deep learning-based brace damage detection for concentrically braced frame structures under seismic loadings

2019 ◽  
Vol 22 (16) ◽  
pp. 3473-3486 ◽  
Author(s):  
Heng Liu ◽  
Yunfeng Zhang
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Learning Model ◽  
Frame Structures ◽  
Braced Frame ◽  
Concentrically Braced Frame ◽  
Seismic Force ◽  
Concentrically Braced ◽  
Deep Learning Model

Automated and robust damage detection tool is needed to enhance the resilience of civil infrastructures. In this article, a deep learning-based damage detection procedure using acceleration data is proposed as an automated post-hazard inspection tool for rapid structural condition assessment. The procedure is investigated with a focus on application in concentrically braced frame structure, a commonly used seismic force-resisting structural system with bracing as fuse members. A case study of six-story concentrically braced frame building was selected to numerically validate and demonstrate the proposed method. The deep learning model, a convolutional neural network, was trained and tested using numerically generated dataset from over 2000 sets of nonlinear seismic simulation, and an accuracy of over 90% was observed for bracing buckling damage detection in this case study. The results of the deep learning model were also discussed and extended to define other damage feature indices. This study shows that the proposed procedure is promising for rapid bracing condition inspection in concentrically braced frame structures after earthquakes.

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