Facade Damage Assessment of the Buildings in Bam, Iran 2003 and Kermanshah, Iran 2017 Earthquakes

Abstract One of the essential factors in buildings frontage is the continuity of the structural and building envelope parts. In this investigation, a comparison was made between Bam and Kermanshah earthquakes. A strong earthquake (magnitude 6.6) struck the city of Bam in southeast Iran on 26 December 2003, and similarly, another strong earthquake struck the city of Kermanshah (magnitude 7.3) in Iran on 12 November 2017. Damage in the facades of the buildings was a clear contributor to the overall building damage. This paper presents the damage assessment of the different facade systems from multi-story buildings in Bam and Kermanshah, Iran. The survey covers the buildings greater than three stories in height, excluding most unreinforced masonry facades. As far as a building can have more than one facade system, any facade systems are evaluated individually. Observation of facade damage is discussed and is presented in terms of its performance level.

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Facade damage assessment of multi-storey buildings in the 2011 Christchurch earthquake

Bulletin of the New Zealand Society for Earthquake Engineering ◽

10.5459/bnzsee.44.4.368-376 ◽

2011 ◽

Vol 44 (4) ◽

pp. 368-376 ◽

Cited By ~ 18

Author(s):

Andrew Baird ◽

Alessandro Palermo ◽

Stefano Pampanin

Keyword(s):

Damage Assessment ◽

Structural Parameters ◽

Central Business District ◽

Unreinforced Masonry ◽

Building Damage ◽

Performance Level ◽

System A ◽

Business District

The magnitude 6.3 earthquake that struck Christchurch on the 22nd February 2011 caused widespread damage to the multi-storey buildings within Christchurch’s central business district (CBD). Damage to the facades of these buildings was a clear contributor to the overall building damage. This paper presents the damage assessment of the facade systems from a survey of 217 multi-storey buildings in the Christchurch CBD. The survey covers only buildings greater than three stories in height, excluding the majority of unreinforced masonry facades, of which damage has been well documented. Since a building can have more than one type of facade system, a total of 371 facade systems are surveyed. Observation of facade damage is discussed and is presented in terms of its performance level. Trends in facade performance are examined in relation the structural parameters such as construction age and height.

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Advanced DInSAR analysis for building damage assessment in large urban areas: an application to the city of Roma, Italy

10.1117/12.2194808 ◽

2015 ◽

Cited By ~ 1

Author(s):

Peppe J. V. D'Aranno ◽

Maria Marsella ◽

Silvia Scifoni ◽

Marianna Scutti ◽

Alberico Sonnessa ◽

...

Keyword(s):

Urban Areas ◽

Damage Assessment ◽

Building Damage ◽

The City

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Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study following the 26 January 2001 Gujarat Earthquake

Earthquake Spectra ◽

10.1193/1.1650865 ◽

2004 ◽

Vol 20 (1) ◽

pp. 145-169 ◽

Cited By ~ 109

Author(s):

Keiko Saito ◽

Robin J. S. Spence ◽

Christopher Going ◽

Michael Markus

Keyword(s):

Damage Assessment ◽

Satellite Images ◽

Satellite Image ◽

Building Damage ◽

Large Area ◽

Rapid Visual Screening ◽

High Resolution Satellite Images ◽

Collapsed Buildings ◽

Optical Satellite Images ◽

The City

Newly available optical satellite images with 1-m ground resolution such as IKONOS mean that rapid postdisaster damage assessment might be made over large areas. Such surveys could be of great value to emergency management and post-event recovery operations and have particular promise for earthquake areas, where damage distribution is often very uneven. In this paper three satellite images taken before and after the 26 January 2001 Gujarat earthquake were studied for damage assessment purposes. The images comprised a post-earthquake cover of the city of Bhuj, which was close to the epicenter, and pre- and post-earthquake cover of the city Ahmedabad. The assessment data was then compared with damage surveys actually made on-site. Three separate experiments were conducted. In the first, the satellite image of Bhuj was compared with detailed ground photos of 28 severely damaged buildings taken at about the same time as the satellite image, to investigate the levels and types of damage that can and cannot be identified. In the second experiment, the whole city center of Bhuj was damage mapped using only the satellite image. This was subsequently compared with a map produced from a building-by-building damage survey. In the third experiment, pre- and post-earthquake images for a large area of Ahmedabad were compared and totally collapsed buildings were identified. These sites were subsequently visited to confirm the accuracy of the observations. The experiment results indicate that rapid visual screening can identify areas of heavy damage and individual collapsed buildings, even when comparative cover does not exist. The need to develop a tool with direct application to support emergency response is discussed.

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Post-Earthquake Rapid Damage Assessment of Road Bridges in Glina County

Buildings ◽

10.3390/buildings12010042 ◽

2022 ◽

Vol 12 (1) ◽

pp. 42

Author(s):

Anđelko Vlašić ◽

Mladen Srbić ◽

Dominik Skokandić ◽

Ana Mandić Ivanković

Keyword(s):

Old Age ◽

Damage Assessment ◽

Strong Earthquake ◽

Structural Damage ◽

Visual Inspection ◽

Structural Condition ◽

The City ◽

The Town ◽

Earthquake Assessment ◽

Housing Units

In December 2020, a strong earthquake occurred in Northwestern Croatia with a magnitude of ML = 6.3. The epicenter of this earthquake was located in the town of Petrinja, about 50 km from Zagreb, and caused severe structural damage throughout Sisak-Moslavina county. One of the biggest problems after this earthquake was the structural condition of the bridges, especially since most of them had to be used immediately for demolition, rescue, and the transport of mobile housing units in the affected areas. Teams of civil engineers were quickly formed to assess the damage and structural viability of these bridges and take necessary actions to make them operational again. This paper presents the results of the rapid post-earthquake assessment for a total of eight bridges, all located in or around the city of Glina. For the assessment, a visual inspection was performed according to a previously established methodology. Although most of the inspected bridges were found to be deteriorated due to old age and lack of maintenance, very few of them showed serious damage from the earthquake, with only one bridge requiring immediate strengthening measures and use restrictions. These measurements are also presented in this paper.

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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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