Stability-based damage assessment and rapid post-disaster quantification of a latticed shell subjected to earthquake loading

2020 ◽  
Vol 206 ◽  
pp. 110020
Author(s):  
Y.G. Li ◽  
H.P. Hong
Keyword(s):  
Damage Assessment ◽  
Earthquake Loading ◽  
Post Disaster

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.

scholarly journals Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas

2018 ◽  
Vol 10 (11) ◽  
pp. 1804 ◽  
Author(s):  
Minyoung Jung ◽  
Junho Yeom ◽  
Yongil Kim
Keyword(s):  
Urban Areas ◽  
Damage Assessment ◽  
Optical Data ◽  
Alos Palsar ◽  
Physical Relationship ◽  
Level Indicator ◽  
Damage Level ◽  
2011 Japan Tsunami ◽  
The Difference ◽  
Post Disaster

Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data.

scholarly journals Geo-spatial Techniques for Rapid Post Disaster Needs Assessment (rPDNA)

2019 ◽  
Vol 8 (4) ◽  
pp. 11198-11206
Keyword(s):  
Needs Assessment ◽  
Texture Analysis ◽  
Damage Assessment ◽  
Data Gathering ◽  
Decision Makers ◽  
Assessment Model ◽  
Geospatial Techniques ◽  
Post Disaster ◽  
Speed And Accuracy ◽  
Critical Challenge

Post Disaster Needs Assessment (PDNA) by conventional techniques is one of the critical challenge to respond and recover in specific timeline. This study aims on providing a rapid damage assessment model (rPDNA) by integrating geospatial techniques to compliment Post Disaster Needs Assessment (PDNA) developed by UNDP, WB and other UN agencies. This model focuses on generating the disaster damage reports within 48 – 72 hours after the disaster, to guide the decision makers on when, how and where to start the PDNA. To improve the speed and accuracy in assessment through rPDNA, various indicators like NDVI, NDWI and texture analysis has been used. Crowdsourcing approach was also adopted to make disaster affected people/victims as volunteers for quick data gathering.

scholarly journals DEEP LEARNING FOR AUTOMATIC BUILDING DAMAGE ASSESSMENT: APPLICATION IN POST-DISASTER SCENARIOS USING UAV DATA

2021 ◽  
Vol V-1-2021 ◽  
pp. 113-120
Author(s):  
A. Calantropio ◽  
F. Chiabrando ◽  
M. Codastefano ◽  
E. Bourke
Keyword(s):  
Deep Learning ◽  
Damage Assessment ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Building Damage ◽  
Research Field ◽  
Future Research ◽  
Damage Classification ◽  
Research Perspectives ◽  
Post Disaster

Abstract. During the last few years, the technical and scientific advances in the Geomatics research field have led to the validation of new mapping and surveying strategies, without neglecting already consolidated practices. The use of remote sensing data for damage assessment in post-disaster scenarios underlined, in several contexts and situations, the importance of the Geomatics applied techniques for disaster management operations, and nowadays their reliability and suitability in environmental emergencies is globally recognized. In this paper, the authors present their experiences in the framework of the 2016 earthquake in Central Italy and the 2019 Cyclone Idai in Mozambique. Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid production of cartography to be used in emergency response operations. The performed analyses have been presented, and the strengths and weaknesses of the employed methods and techniques have been outlined. In conclusion and based on the authors' experience, some operational suggestions and best practices are provided and future research perspectives within the same research topic are introduced.

Role of Damaged Heritage Rapid Assessment in Post-Disaster Recovery Program

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Punto Wijayanto
Keyword(s):  
Cultural Heritage ◽  
Damage Assessment ◽  
Rapid Assessment ◽  
Title Page ◽  
Main Concern ◽  
The Government ◽  
Recovery Program ◽  
2004 Tsunami ◽  
Post Disaster

<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Indonesia is a country located in the ring of </span>fire. Various kinds of disasters threats parts of Indonesia, including its rich cultural and natural heritage assets. Since the 2004, Tsunami in Aceh, the government gives serious attention to disaster. In 2007, it stipulated the Law 24/2007 on Disaster Management. It's so unfortunate that cultural heritage is not yet part of main concern during disaster programs. In addition, there are only few experiences in the world about how to deal with the condition of heritage affected by disaster.</p></div></div></div></div></div></div><div class="layoutArea"><div class="column"><p><span>Heritage organizations in Indonesia aim to raise awareness about disaster risks on cultural heritage. They develop system of damage assessment to cultural heritage or Damage Heritage Rapid Assessment (DHRA) at the time of emergencies. Damage assessment was introduced in Yogyakarta, experienced a lot of damage caused by the massive earthquake in 2006. DHRA has been used in Padang (2009), Yogyakarta (2010), Jakarta (2013) and Manado (2014). This paper aims to explain what DHRA is and how DHRA can contribute to post-disaster rehabilitation and reconstruction of heritage district. </span></p><div class="layoutArea"><div class="column"><p>Keywords: Damage assessment, disaster, heritage </p></div></div></div></div></div>

Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA)

2018 ◽  
Vol 13 (7) ◽  
pp. 1257-1271
Author(s):  
Erick Mas ◽  
Daniel Felsenstein ◽  
Luis Moya ◽  
A. Yair Grinberger ◽  
Rubel Das ◽  
...  
Keyword(s):  
Disaster Management ◽  
Damage Assessment ◽  
Large Scale ◽  
Target Population ◽  
Integrated Model ◽  
Urban Resilience ◽  
Agent Based ◽  
Urban Dynamics ◽  
Socioeconomic Analysis ◽  
Post Disaster

The DIM2SEA research project aims to increase urban resilience to large-scale disasters. We are engaged in developing a prototype Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA) that will give disaster officials, stakeholders, urban engineers and planners an analytic tool for mitigating some of the worst excesses of catastrophic events. This is achieved by harnessing state-of-the-art developments in damage assessment, spatial simulation modeling, and Geographic Information System (GIS). At the heart of DIM2SEA is an agent-based model combined with post-disaster damage assessment and socioeconomic impact models. The large amounts of simulated spatial and temporal data generated by the agent-based models are fused with the socioeconomic profiles of the target population to generate a multidimensional database of inherently “synthetic” big data. Progress in the following areas is reported here: (1) Synthetic population generation from census tract data into agent profiling and spatial allocation, (2) developing scenarios of building damage due to earthquakes and tsunamis, (3) building debris scattering estimation and road network disruption, (4) logistics regarding post-disaster relief distribution, (5) the labor market in post-disaster urban dynamics, and (6) household insurance behavior as a reflection of urban resilience.

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