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Empirical fragility curves for Italian residential RC buildings
In this paper, empirical fragility curves for reinforced concrete buildings are derived, based on post-earthquake damage data collected in the aftermath of earthquakes occurred in Italy in the period 1976–2012. These data, made available through an online platform called Da.D.O., provide information on building position, building characteristics and damage detected on different structural components. A critical review of this huge amount of data is carried out to guarantee the consistency among all the considered databases. Then, an in-depth analysis of the degree of completeness of the survey campaign is made, aiming at the identification of the Municipalities subjected to a partial survey campaign, which are discarded from fragility analysis. At the end of this stage, only the Irpinia 1980 and L’Aquila 2009 databases are considered for further elaborations, as fully complying with these criteria. The resulting database is then integrated with non-inspected buildings sited in less affected areas (assumed undamaged), to account for the negative evidence of damage. The PGA evaluated from the shakemaps of the Italian National Institute of Geophysics and Volcanology (INGV) and a metric based on six damage levels according to EMS-98 are used for fragility analysis. The damage levels are obtained from observed damage collected during post-earthquake inspections through existing conversion rules, considering damage to vertical structures and infills/partitions. The maximum damage level observed on vertical structures and infills/partitions is then associated to the whole building. Fragility curves for two vulnerability classes, C2 and D, further subdivided into three classes of building height, are obtained from those derived for specific structural typologies (identified based on building height and type of design), using their frequency of occurrence at national level as weights.
Journal Article
Flood damage and risk assessment for urban area in Malaysia
In recent years, flood risk map has been widely accepted as a tool for flood mitigation. The risk of flooding is normally illustrated in terms of its hazard (flood inundation maps), while vulnerability emphasizes the consequences of flooding. In developing countries, published studies on flood vulnerability assessment are limited, especially on flood damage. This paper attempts to establish a flood damage and risk assessment framework for Segamat town in Johor, Malaysia. A combination of flood hazard (flood characteristics), exposure (value of exposed elements), and vulnerability (flood damage function curve) were used for estimating the flood damage. The flood depth and areal extent were obtained from flood modeling and mapping using HEC-HMS/RAS and Arc GIS, respectively. Expected annual damage (EAD) for residential areas (50,112 units) and commercial areas (9,318 premises) were RM12.59 million and RM2.96 million, respectively. The flood hazard map shows that Bandar Seberang area (46,184 properties) was the most affected by the 2011 flood. The flood damage map illustrates similar patterns, with Bandar Seberang suffering the highest damage. The damage distribution maps are useful for reducing future flood damage by identifying properties with high flood risk.
Journal Article
The 30 October 2020 Aegean Sea Tsunami: Post-Event Field Survey Along Turkish Coast
On 30 October 2020, a strong normal-faulting earthquake struck Samos Island in Greece and İzmir Province in Turkey, both in the eastern Aegean Sea. The earthquake generated a tsunami that hit the coasts of Samos Island, Greece and İzmir, Turkey. National teams performed two post-tsunami field surveys on 31 October to 1 November 2020, and 4–6 November 2020, along the Turkish coastline; while the former was a quick survey on the days following the tsunami, the latter involved more detailed measurement and investigation focusing on a ~ 110-km-long coastline extending from Alaçatı (Çeşme District of İzmir) to Gümüldür (Menderes District of İzmir). The survey teams measured runup and tsunami heights, flow depths, and inundation distances at more than 120 points at eight different localities. The largest tsunami runup among the surveyed locations was measured as 3.8 m in Akarca at a distance of 91 m from the shoreline. The maximum tsunami height of 2.3 m (with a flow depth of 1.4 m) was observed at Kaleiçi region in Sığacık, where the most severe tsunami damage was observed. There, the maximum runup height was measured as 1.9 m at the northeastern side of the bay. The survey team also investigated tsunami damage to coastal structures, noticing a gradual decrease in the impact from Gümüldür to further southeast. The findings of this field survey provide insights into the coastal impact of local tsunamis in the Aegean Sea.
Journal Article
Remote sensing‐based mapping of structural building damage in the Ahr valley
Flood damage data are needed for various applications. Structural damage of buildings can reflect not only the economic damage but also the life‐threatening condition of a building, which provide crucial information for disaster response and recovery. Since traditional on‐site data collection shortly after a disaster is challenging, remote sensing data can be of great help, cover a wider area and be deployed earlier in time than on‐site surveys. However, this has its challenges and limitations. We elucidate on that by presenting two case studies from flash floods in Germany. First, we assessed the reliability of an existing flood damage schema, which differentiates from minor (structural) damage to complete building collapse. We compared two on‐site raters of the 2016 Braunsbach flood, reaching an excellent level of reliability. Second, we mapped structural building damage after the flood in the Ahr valley in 2021 using a textured 3D mesh and orthophotos. Here, we evaluated the remote sense‐based damage mapping done by three raters. Although the heterogeneity of ratings using remote sensing data is larger than among on‐site ratings, we consider it fit‐for‐purpose when compared with on‐site mapping, especially for event documentation and as basis for financial damage estimation and less complex numerical modelling.
Journal Article
Scalable approach to create annotated disaster image database supporting AI-driven damage assessment
As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization.
Journal Article