Re-evaluation of building damage during recent earthquakes in Turkey

2008 ◽  
Vol 30 (2) ◽  
pp. 412-427 ◽  
Author(s):  
Mehmet Inel ◽  
Hayri Baytan Ozmen ◽  
Huseyin Bilgin
Keyword(s):  
Building Damage ◽  
Recent Earthquakes

Failure Analysis Of Buried Gas Pipelines Crossing Seismic Faults

2020 ◽  
Vol 3 (2) ◽  
pp. 781-790
Author(s):  
M. Rizwan Akram ◽  
Ali Yesilyurt ◽  
A.Can. Zulfikar ◽  
F. Göktepe
Keyword(s):  
Ground Deformation ◽  
Warning System ◽  
Strike Slip ◽  
Gas Pipelines ◽  
Steel Pipes ◽  
Seismic Fault ◽  
Fault Parameters ◽  
Dip Angle ◽  
Permanent Ground Deformation ◽  
Recent Earthquakes

Research on buried gas pipelines (BGPs) has taken an important consideration due to their failures in recent earthquakes. In permanent ground deformation (PGD) hazards, seismic faults are considered as one of the major causes of BGPs failure due to accumulation of impermissible tensile strains. In current research, four steel pipes such as X-42, X-52, X-60, and X-70 grades crossing through strike-slip, normal and reverse seismic faults have been investigated. Firstly, failure of BGPs due to change in soil-pipe parameters have been analyzed. Later, effects of seismic fault parameters such as change in dip angle and angle between pipe and fault plane are evaluated. Additionally, effects due to changing pipe class levels are also examined. The results of current study reveal that BGPs can resist until earthquake moment magnitude of 7.0 but fails above this limit under the assumed geotechnical properties of current study. In addition, strike-slip fault can trigger early damage in BGPs than normal and reverse faults. In the last stage, an early warning system is proposed based on the current procedure. 

Notices of recent earthquakes; No. 4

1874 ◽  
Vol s3-7 (40) ◽  
pp. 384-387
Author(s):  
C. G. Rockwood
Keyword(s):  
Recent Earthquakes

TSUNAMI ACEH 2004 SEBAGAI DASAR PENATAAN RUANG KOTA MEULABOH

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Wisyanto Wisyanto
Keyword(s):  
Coastal Area ◽  
Building Damage ◽  
Tsunami Hazard ◽  
The Road ◽  
Tsunami Disaster ◽  
Landuse Planning ◽  
The Future ◽  
Tsunami Mitigation ◽  
Potential Losses ◽  
The Impact

Tsunami which was generated by the 2004 Aceh eartquake has beenhaunting our life. The building damage due to the tsunami could be seenthroughout Meulaboh Coastal Area. Appearing of the physical loss wasclose to our fault. It was caused by the use dan plan of the land withoutconsidering a tsunami disaster threat. Learning from that event, we haveconducted a research on the pattern of damage that caused by the 2004tsunami. Based on the analysis of tsunami hazard intensity and thepattern of building damage, it has been made a landuse planning whichbased on tsunami mitigation for Meulaboh. Tsunami mitigation-based ofMeulaboh landuse planning was made by intergrating some aspects, suchas tsunami protection using pandanus greenbelt, embankment along withhigh plants and also arranging the direction of roads and setting of building forming a rhombus-shaped. The rhombus-shaped of setting of the road and building would reduce the impact of tsunamic wave. It is expected that these all comprehensive landuse planning will minimize potential losses in the future .

Applications of Drone for Building Damage Survey in a Disaster

2019 ◽  
Vol 57 (9) ◽  
pp. 733-742
Author(s):  
Y. Maida ◽  
T. Mukai ◽  
H. Miyauchi
Keyword(s):  
Building Damage ◽  
Damage Survey

scholarly journals ShakeDaDO: A data collection combining earthquake building damage and ShakeMap parameters for Italy

2020 ◽  
Vol 1 ◽  
pp. 36-51
Author(s):  
Licia Faenza ◽  
Alberto Michelini ◽  
Helen Crowley ◽  
Barbara Borzi ◽  
Marta Faravelli
Keyword(s):  
Data Collection ◽  
Building Damage

scholarly journals Broadband Strong-Motion Prediction for Future Nankai-Trough Earthquakes Using Statistical Green’s Function Method and Subsequent Building Damage Evaluation

2021 ◽  
Vol 11 (15) ◽  
pp. 7041
Author(s):  
Baoyintu Baoyintu ◽  
Naren Mandula ◽  
Hiroshi Kawase
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Nankai Trough ◽  
Ground Motions ◽  
Building Damage ◽  
Steel Buildings ◽  
Ground Acceleration ◽  
Attenuation Relations ◽  
Strong Ground Motions ◽  
Strong Ground

We used the Green’s function summation method together with the randomly perturbed asperity sources to sum up broadband statistical Green’s functions of a moderate-size source and predict strong ground motions due to the expected M8.1 to 8.7 Nankai-Trough earthquakes along the southern coast of western Japan. We successfully simulated seismic intensity distributions similar to the past earthquakes and strong ground motions similar to the empirical attenuation relations of peak ground acceleration and velocity. Using these results, we predicted building damage by non-linear response analyses and find that at the regions close to the source, as well as regions with relatively thick, soft sediments such as the shoreline and alluvium valleys along the rivers, there is a possibility of severe damage regardless of the types of buildings. Moreover, the predicted damage ratios for buildings built before 1981 are much higher than those built after because of the significant code modifications in 1981. We also find that the damage ratio is highest for steel buildings, followed by wooden houses, and then reinforced concrete buildings.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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