Estimation of Potential Seismic Damage in Urban Areas

2015 ◽  
pp. 1012-1020 ◽  
Author(s):  
Aysegul Askan ◽  
Murat Altug Erberik ◽  
Mustafa Bilal
Keyword(s):  
Urban Areas ◽  
Seismic Damage

scholarly journals Seismic damage simulation in urban areas based on a high-fidelity structural model and a physics engine

2013 ◽  
Vol 71 (3) ◽  
pp. 1679-1693 ◽  
Author(s):  
Zhen Xu ◽  
Xinzheng Lu ◽  
Hong Guan ◽  
Bo Han ◽  
Aizhu Ren
Keyword(s):  
Urban Areas ◽  
Structural Model ◽  
Seismic Damage ◽  
High Fidelity ◽  
Physics Engine ◽  
Damage Simulation

Estimation of Potential Seismic Damage in Urban Areas

2014 ◽  
pp. 1-9 ◽  
Author(s):  
Aysegul Askan ◽  
M. Altug Erberik ◽  
Mustafa Bilal
Keyword(s):  
Urban Areas ◽  
Seismic Damage

scholarly journals Vibration-Based Seismic Damage States Evaluation for Regional Concrete Beam Bridges Using Random Forest Method

2020 ◽  
Vol 12 (12) ◽  
pp. 5106 ◽  
Author(s):  
Xiaoming Lei ◽  
Limin Sun ◽  
Ye Xia ◽  
Tiantao He
Keyword(s):  
Random Forest ◽  
Urban Areas ◽  
Evaluation Method ◽  
Prediction Models ◽  
Seismic Damage ◽  
Critical Parameters ◽  
Good Prediction ◽  
Damage State ◽  
Structural Dynamic ◽  
Intensity Measures

Transportation networks play an important role in urban areas, and bridges are the most vulnerable structures to earthquakes. The seismic damage evaluation of bridges provides an effective tool to assess the potential damage, and guides the post-earthquake recovery operations. With the help of structural health monitoring (SHM) techniques, the structural condition could be accurately evaluated through continuous monitoring of structural responses, and evaluating vibration-based features, which could reflect the deterioration of materials and boundary conditions, and are extensively used to reflect the structural conditions. This study proposes a vibration-based seismic damage state evaluation method for regional bridges. The proposed method contains the measured structural dynamic parameters and bridge configuration parameters. In addition, several intensity measures are also included in the model, to represent the different characteristics and the regional diversity of ground motions. The prediction models are trained with a random forest algorithm, and their confusion matrices and receiver operation curves reveal a good prediction performance, with over 90% accuracy. The significant parameter identification of bridge systems and components reveals the critical parameters for seismic design, disaster prevention and structure retrofit.

Management of minor ailments in primary schoolchildren in rural and urban areas

1996 ◽  
Vol 22 (3) ◽  
pp. 167-174
Author(s):  
J A Cantrill ◽  
B Johannesson ◽  
M Nicholson ◽  
P R Noyce
Keyword(s):  
Urban Areas ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Primary Schoolchildren ◽  
Minor Ailments
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