Study on the Strengthening Effect of a Typical RC Frame in Wenchuan Earthquake with Attached Substructures

2013 ◽  
Vol 353-356 ◽  
pp. 2057-2064
Author(s):  
Sheng Nan Huang ◽  
Xin Zheng Lu ◽  
Lie Ping Ye
Keyword(s):  
Wenchuan Earthquake ◽  
Incremental Dynamic Analysis ◽  
Critical Parameters ◽  
Strengthening Effect ◽  
Rc Frame ◽  
Viscous Damper ◽  
Buckling Restrained Brace ◽  
The Wenchuan Earthquake ◽  
Collapse Resistance ◽  
Better Than

This work is based on a typical RC frame that was closed to the epicenter and collapsed during the Wenchuan Earthquake. The seismic collapse resistance of the frame was strengthened by attached substructures, including conventional brace, buckling restrained brace (BRB) and viscous damper. Collapse fragility analysis based on incremental dynamic analysis is implemented for each strengthening scheme to compare their effects and to analyze the influence of critical parameters. The results show that the viscous damper performs better than the BRB, and the BRB performs better than the conventional brace. With the same strengthening parameters, the A-shaped bracing scheme is better than the X-shaped scheme.

Study on Destructive Modes of RC Frame Columns in Wenchuan Earthquake

2011 ◽  
Vol 250-253 ◽  
pp. 1597-1601
Author(s):  
Cheng Qing Liu ◽  
Wei Xing Shi ◽  
Shi Chun Zhao ◽  
Yi Pan
Keyword(s):  
Reinforced Concrete ◽  
Wenchuan Earthquake ◽  
Concrete Columns ◽  
Rc Frame ◽  
Reinforced Concrete Columns ◽  
Damage Characteristics ◽  
The Wenchuan Earthquake ◽  
Earthquake Damages

Typical earthquake damages of Reinforced concrete columns in the Wenchuan earthquake are introduced, and the damage characteristics of concrete columns are described, then several typical destructive modes of Reinforced concrete columns are summarized. Further, various reasons of destructive modes are analyzed. Final, some practical advices are proposed to reduce the damages of reinforced concrete columns.

A Hysteretic Model of Conventional Steel Braces and an Analysis of the Collapse Prevention Effect of Brace Strengthening

2012 ◽  
Vol 174-177 ◽  
pp. 3-10 ◽  
Author(s):  
Sheng Nan Huang ◽  
Xin Zheng Lu ◽  
Lie Ping Ye
Keyword(s):  
Dynamic Analysis ◽  
Incremental Dynamic Analysis ◽  
Hysteretic Behavior ◽  
Fragility Analysis ◽  
Strengthening Effect ◽  
Rc Frame ◽  
Hysteretic Model ◽  
Collapse Prevention ◽  
Conventional Steel ◽  
Rc Frames

A hysteretic model of conventional steel braces consisting of 18 parameters is proposed. This model is able to simulate the hysteretic behavior of conventional steel braces accurately. The collapse-prevention strengthening effect with steel braces for a typical reinforced concrete (RC) frame that was close to the epicenter and collapsed during the Great Wenchuan Earthquake is discussed via push-over analysis and collapse fragility analysis based on incremental dynamic analysis. The result could be referred to for the seismic collapse prevention design of RC frames.

Catastrophic debris flows triggered by the 20 August 2019 rainfall, a decade since the Wenchuan earthquake, China

Landslides ◽  
2021 ◽  
Author(s):  
Fan Yang ◽  
Xuanmei Fan ◽  
Srikrishnan Siva Subramanian ◽  
Xiangyang Dou ◽  
Junlin Xiong ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Debris Flows ◽  
The Wenchuan Earthquake

scholarly journals Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5191
Author(s):  
Chang Li ◽  
Bangjin Yi ◽  
Peng Gao ◽  
Hui Li ◽  
Jixing Sun ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Remote Sensing Images ◽  
Machine Learning Methods ◽  
The Wenchuan Earthquake ◽  
Causative Factors ◽  
Multiple Structures ◽  
Sample Set ◽  
Larger Sample ◽  
Structure Layer ◽  
Earthquake Area

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.

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