Detection of collapsed buildings an earthquake using photogrammetric methods and analysis of the effects of soil and structure parameters on collapses: A case study of the Elbistan-Pazarcik earthquakes

On February 6, 2023, two major earthquakes (Mw 7.7 and 7.6) struck with a nine-hour interval centered in Kahramanmaraş, Elbistan-Pazarcık, causing severe destruction in 11 provinces in Turkey. Following the earthquake, many organizations participated in damage assessment efforts, identifying collapsed buildings as well as those with minor, moderate, and severe damage over several months. The media extensively covered the issues related to the construction materials used in the collapsed buildings and the negligence of the construction companies during the building phase, which led to significant loss of life, keeping the topic in the spotlight of Turkey's agenda for a long time. Post-earthquake, civil engineers focused on structural inspection, structural integrity, and earthquake loads on buildings, while geologists and geophysicists predominantly provided insights about the faults where the earthquake occurred, the current energy states of these faults, and the potential future earthquakes. However, topics such as the automatic detection of collapsed structures, and the statistical analysis of building attributes and ground conditions on both collapsed and non-collapsed buildings were overlooked. Considering the need for the immediate identification of directly impacted buildings after the earthquake, high-resolution aerial photographs, digital surface models (DSMs) with low ground sampling distance (GSD), verification of the accuracy of spatial data, and change detection analyses are required. This situation clearly demonstrates the necessity for the involvement of geomatics engineering, in addition to other engineering disciplines directly related to earthquakes, ground types, and structural integrity. Consequently, not only the production of highly accurate base data but also the verification of the accuracy of all base data, conducting change analyses, obtaining building-ground attribute information from geographic information systems (GIS) databases, classifying them for special analyses, including geological and structural data in analyses based on earthquake regulation standards and approvals from relevant engineering fields, and statistically evaluating these attributes can result in more meaningful outcomes that can be explained through location-based and statistical data. In this thesis, the automatic detection of collapsed buildings among 62,549 buildings affected by the Kahramanmaraş-centered earthquakes was performed with high accuracy (90%) using high-accuracy geographical data (DSM and aerial photographs). Subsequently, the impact of collapses was examined by adding attribute information relevant to the structural integrity of all collapsed and non-collapsed buildings from the topological database (such as building height, number of floors, basement factor). The ground-building attributes were statistically evaluated within the scope of earthquake regulations by matching three local ground classes produced using VS30 ground stiffness values and classified "terrain slope" produced using high-accuracy digital terrain models. These analyses demonstrate the feasibility of analyzing geographical data in the shortest time possible before and after a disaster to reveal the impacts of the disaster promptly, and subsequently, conducting studies to determine risk areas, safe areas, and zoning areas, considering future earthquakes and other disasters.