Effect of Sensor Error on the Assessment of Seismic Building Damage

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 .

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Applications of Drone for Building Damage Survey in a Disaster

Concrete Journal ◽

10.3151/coj.57.9_733 ◽

2019 ◽

Vol 57 (9) ◽

pp. 733-742

Author(s):

Y. Maida ◽

T. Mukai ◽

H. Miyauchi

Keyword(s):

Building Damage ◽

Damage Survey

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ANALYSIS OF BUILDING DAMAGE CAUSED BY TSUNAMI AND DEVELOPMENT OF TSUNAMI FRAGILITY CURVES CONSIDERING THE BUILDING AGE

Journal of Japan Society of Civil Engineers Ser B2 (Coastal Engineering) ◽

10.2208/kaigan.76.2_i_709 ◽

2020 ◽

Vol 76 (2) ◽

pp. I_709-I_714

Author(s):

Ryu MIYAMOTO ◽

Anawat SUPPASRI ◽

Fumihiko IMAMURA

Keyword(s):

Fragility Curves ◽

Building Damage ◽

Tsunami Fragility

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

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.01.002 ◽

2020 ◽

Vol 1 ◽

pp. 36-51

Author(s):

Licia Faenza ◽

Alberto Michelini ◽

Helen Crowley ◽

Barbara Borzi ◽

Marta Faravelli

Keyword(s):

Data Collection ◽

Building Damage

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Spectral sensor error analysis for measuring x-ray radiation drive using the DANTE diagnostic toward inertial confinement fusion experiments

Review of Scientific Instruments ◽

10.1063/5.0035584 ◽

2021 ◽

Vol 92 (3) ◽

pp. 033505

Author(s):

C. D. Harris ◽

G. E. Kemp ◽

M. B. Schneider ◽

K. Widmann ◽

M. S. Rubery ◽

...

Keyword(s):

Error Analysis ◽

Inertial Confinement Fusion ◽

Inertial Confinement ◽

X Ray ◽

Sensor Error ◽

Confinement Fusion ◽

Fusion Experiments

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

Applied Sciences ◽

10.3390/app11157041 ◽

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.

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A Modified CGM Algorithm Enhances Data Availability While Retaining iCGM Performance

Journal of Diabetes Science and Technology ◽

10.1177/19322968211007521 ◽

2021 ◽

pp. 193229682110075

Author(s):

Rebecca A. Harvey Towers ◽

Xiaohe Zhang ◽

Rasoul Yousefi ◽

Ghazaleh Esmaili ◽

Liang Wang ◽

...

Keyword(s):

Data Integration ◽

Patient Experience ◽

Real World ◽

Data Availability ◽

Post Processing ◽

Sensor Error ◽

The Mean ◽

The Difference ◽

Data Gap ◽

Improve Patient

The algorithm for the Dexcom G6 CGM System was enhanced to retain accuracy while reducing the frequency and duration of sensor error. The new algorithm was evaluated by post-processing raw signals collected from G6 pivotal trials (NCT02880267) and by assessing the difference in data availability after a limited, real-world launch. Accuracy was comparable with the new algorithm—the overall %20/20 was 91.7% before and 91.8% after the algorithm modification; MARD was unchanged. The mean data gap due to sensor error nearly halved and total time spent in sensor error decreased by 59%. A limited field launch showed similar results, with a 43% decrease in total time spent in sensor error. Increased data availability may improve patient experience and CGM data integration into insulin delivery systems.

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

Remote Sensing ◽

10.3390/rs13050905 ◽

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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Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Remote Sensing ◽

10.3390/rs13061146 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1146

Author(s):

Yuliang Nie ◽

Qiming Zeng ◽

Haizhen Zhang ◽

Qing Wang

Keyword(s):

Damage Detection ◽

Texture Features ◽

Classification Performance ◽

Building Damage ◽

Alos Palsar ◽

Yushu Earthquake ◽

Matching Algorithm ◽

2011 Tohoku Tsunami ◽

Collapsed Buildings ◽

Study Sites

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

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