Building Inspection and Damage Data for the 2002 Molise, Italy, Earthquake

2004 ◽  
Vol 20 (1_suppl) ◽  
pp. 167-190 ◽  
Author(s):  
Agostino Goretti ◽  
Giacomo Di Pasquale
Keyword(s):  
Damage Assessment ◽  
Building Damage ◽  
Masonry Buildings ◽  
Public Servants ◽  
Building Type ◽  
Damage Data ◽  
Observed Damage ◽  
Modal Values ◽  
Slight Damage

Shortly after the October 31, 2002, Molise, Italy, earthquake, a widespread fitness-for-service and building damage assessment was launched. In two months, more than 23,000 buildings were inspected using a standardized damage assessment form. As many as 100 inspection teams, consisting of public servants and volunteer professionals, totaled approximately 80,000 person-hours. Analysis of the collected building type and damage data shows high-vulnerability masonry buildings with significant preexisting damage. With the sole exception of San Giuliano, the modal values of the observed damage occurred for the negligible-to-slight damage levels D=0 or D=1, with only a few buildings showing higher damage levels. Nevertheless, due to their high vulnerability, about 40% of the inspected buildings were unusable, with important consequences for the number of people needing shelter. The survey made it possible to determine the usability of about 12,000 buildings and the repairs needed for about 1,000 buildings.

scholarly journals Evolution of the seismic vulnerability of masonry buildings based on the damage data from L'Aquila 2009 event

2021 ◽  
Author(s):  
Carlo Del Gaudio ◽  
Santa Anna Scala ◽  
Paolo Ricci ◽  
Gerardo M. Verderame
Keyword(s):  
Building Materials ◽  
Seismic Vulnerability ◽  
Optimization Technique ◽  
Masonry Buildings ◽  
Seismic Behaviour ◽  
Horizontal Structure ◽  
2009 L’Aquila Earthquake ◽  
Damage Data ◽  
Motion Characterization ◽  
Observed Damage

AbstractThe purpose of this study is the analysis of vulnerability trends, with particular emphasis to the evolution of the seismic behaviour of masonry buildings over the years due to the improvements in construction practices and to the enhancement of building materials over the years, also related to the subsequent enactment of seismic prescriptions. To this aim, residential masonry buildings damaged after the 2009 L'Aquila earthquake are considered, coming from the online platform Da.D.O. (Database di Danno Osservato, Database of Observed Damage) recently released from the Italian Department of Civil Protection. General features of all the parameters available from the original database are thoroughly analysed, a selection of which is used for vulnerability analysis, namely the period of construction and the design type, the presence of structural interventions, the type of horizontal structure. Vulnerability curves are obtained through an optimization technique, minimizing the deviation between observed and predicted damage. PGA from ShakeMap is used for ground motion characterization. Damage levels defined according to the European Macroseismic Scale are considered, obtained from the observed damage for vertical structures collected during the inspections. Vulnerability curves are firstly obtained as a function of period of construction and horizontal structural types, limited to the irregular layout and bad quality vertical type only, highlighting their clear influence on seismic behaviour. Lastly, the effectiveness of retrofit intervention is evaluated comparing the vulnerability curves for strengthened masonry buildings compared to those not subjected to any retrofit interventions.

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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