Rapid building damage assessment using EROS B data: the case study of L’Aquila earthquake

Abstract. Buildings in subsiding areas may suffer from settlements causing damages of different severity levels with high impact in terms of yearly economic losses. In these contexts, a systematic damage assessment jointly with continuous monitoring of relevant parameters (e.g. settlements exhibited by points located on the roof) can be extremely useful to control the building behaviour and develop forecasting models. In this regard, the paper presents the results of an integrated analysis carried out on a subsidence-affected urban area in the Netherlands where the availability of multi-temporal building damage surveys and a long DInSAR monitoring dataset allowed both retrieving quantitative empirical relationships between the cause (magnitude of the selected intensity parameter, IP) and the effect (recorded damage severity level, DL) and generating empirical fragility and vulnerability curves. The results pointed out the importance of considering the exact dating of the onset of building damage and the corresponding magnitude of the considered IP in the generation of quantitative forecasting models.

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Post-earthquake damage assessment of traditional masonry buildings: A case study of Bhaktapur municipality following 2015 Gorkha (Nepal) earthquake

Engineering Failure Analysis ◽

10.1016/j.engfailanal.2021.105277 ◽

2021 ◽

Vol 123 ◽

pp. 105277

Author(s):

Manjip Shakya ◽

Chandra Kiran Kawan ◽

Arjun Kumar Gaire ◽

Sunil Duwal

Keyword(s):

Damage Assessment ◽

Earthquake Damage ◽

Masonry Buildings ◽

Nepal Earthquake ◽

2015 Gorkha Nepal Earthquake ◽

Earthquake Damage Assessment

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Case Study of a Heavily Damaged Building during the 2016 MW 7.8 Ecuador Earthquake: Directionality Effects in Seismic Actions and Damage Assessment

Geosciences ◽

10.3390/geosciences11020074 ◽

2021 ◽

Vol 11 (2) ◽

pp. 74

Author(s):

Luis A. Pinzón ◽

Luis G. Pujades ◽

Irving Medranda ◽

Rodrigo E. Alva

Keyword(s):

Damage Assessment ◽

Strong Motion ◽

Response Spectra ◽

Seismic Action ◽

Damage State ◽

Ground Acceleration ◽

Intensity Measures ◽

Capacity Curves ◽

Building Orientation

In this work, the directionality effects during the MW 7.8 earthquake, which occurred in Muisne (Ecuador) on 16 April 2016, were analyzed under two perspectives. The first one deals with the influence of these effects on seismic intensity measures (IMs), while the second refers to the assessment of the expected damage of a specific building located in Manta city, Ecuador, as a function of its azimuthal orientation. The records of strong motion in 21 accelerometric stations were used to analyze directionality in seismic actions. At the closest station to the epicenter (RRup = 20 km), the peak ground acceleration was 1380 cm/s2 (EW component of the APED station). A detailed study of the response spectra ratifies the importance of directionality and confirms the need to consider these effects in seismic hazard studies. Differences between IMs values that consider the directionality and those obtained from the as-recorded accelerograms are significant and they agree with studies carried out in other regions. Concerning the variation of the expected damage with respect to the building orientation, a reinforced concrete building, which was seriously affected by the earthquake, was taken as a case study. For this analysis, the accelerograms recorded at a nearby station and detailed structural documentation were used. The ETABS software was used for the structural analysis. Modal and pushover analyses were performed, obtaining capacity curves and capacity spectra in the two main axes of the building. Two advanced methods for damage assessment were used to obtain fragility and mean damage state curves. The performance points were obtained through the linear equivalent approximation. This allows estimation and analysis of the expected mean damage state and the probability of complete damage as functions of the building orientation. Results show that the actual probability of complete damage is close to 60%. This fact is mainly due to the greater severity of the seismic action in one of the two main axes of the building. The results are in accordance with the damage produced by the earthquake in the building and confirm the need to consider the directionality effects in damage and seismic risk assessments.

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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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