On February 6, 2023, two major earthquakes with magnitudes Mw = 7.7 and Mw = 7.6 struck southeastern Turkiye, causing catastrophic damage and loss of life across 11 provinces, including Malatya. This study focuses on documenting the geotechnical observations and structural damage in Dogansehir, one of the hardest-hit districts not only in Malatya but in the entire affected region. An overview of the-region's tectonic and geological background is presented, followed by an analysis of ground motion data specific to Malatya. A detailed examination of seismic data from stations near Dogansehir was provided to better understand the seismic demands during the earthquakes. The paper then provides insights into the geotechnical conditions, building characteristics, and a damage ratio map of Dogansehir. The influence of local tectonics and geology on the observed damage is analyzed, alongside an evaluation of the seismic performance of masonry and reinforced concrete structures. Dogansehir, located near the epicenters of the Kahramanmaras earthquakes, suffered major structural damage. This was due to the surface rupture occurring near the settlement areas, the establishment of the district centre on the alluvial soil layer and the deficiencies/errors in the building systems. Building settlements on or near active fault zones, as well as on soft soil, leads to serious consequences and should be avoided or require special precautions.

The main causes of damage include poor site selection, such as building on fault lines or on fill soil, as well as deficiencies in design, materials, and workmanship. Damage levels are also linked to the economic conditions of the region. In the 1939 earthquake, there were high casualties due to the magnitude of the earthquake, lack of engineering design in traditional structures and unsuitable soil conditions. Similarly, in the 1992 earthquake, unexpected damage occurred due to faulty designs created by inexperienced engineers who lacked sufficient knowledge of the seismic behavior of structures, errors in craftsmanship and workmanship, and unsuitable residential area selection for construction. These problems continue today and put most of the building stock at risk in case of a major earthquake. Seismic steel isolators are used in two new buildings in the city; if they are effective, they should be made mandatory in new construction. Otherwise, consideration should be given to relocating the city to the more stable southern rocky areas, which were unaffected in both 1939 and 1992.

The 7.7 and 7.6 magnitude Pazarc & imath;k and Elbistan earthquakes that struck Kahramanmara & scedil; on 6 February 2023 caused widespread structural damage across many provinces and are considered rare in seismological terms. While many reinforced concrete (RC) buildings designed under current earthquake regulations sustained significant damage, some older RC buildings with outdated designs sustained only moderate damage. This study aims to analyze the seismic performance of such older RC buildings to understand why they did not collapse or suffer severe damage. An 8-story RC building in Ad & imath;yaman province, damaged by the earthquake, was considered for analysis. The region's seismicity and local site conditions were assessed through borehole operations, geotechnical laboratory tests, and seismic field tests. The soil profile was modeled, and one-dimensional seismic site response analyses were performed using records from nearby stations (TK 4615 Pazarc & imath;k and TK 4612 G & ouml;ksun stations) to determine the foundation-level earthquake record. Nonlinear static pushover analysis was carried out via SAP2000 and STA4CAD, utilizing site response analysis and test results taken from the reinforcement and concrete samples of the building. The findings, compared with the observed damage, provide insights into the performance of older RC buildings in this region.

To date, the September 19, 1985 Michoac & aacute;n (Ms = 8.1) and the September 19, 2017 PueblaMorelos (Mw = 7.1) earthquakes have been the most devastating seismic events in Mexico City. During the 1985 earthquake, 13 important public hospital buildings collapsed or were demolished and 5800 hospital beds were lost. During the 2017 earthquake, 85 buildings of the medical sector were disturbed, two major public hospital were demolished and 1147 hospital beds were affected. In this paper, the author concentrates both in reviewing what occurred during the 1985 earthquake, and in reporting what it has been observed for the 2017 earthquake. From the structural viewpoint, the observed damage is discussed in relationships to: a) seismic codes, b) spectral demands, b) structural irregularities, c) soil settlements, d) tilting, e) structural pounding and, f) deterioration. The observed damaged inventory is also put into perspective with respect to the approximate number of medical facilities that are available in Mexico City. An instantaneous drop of seismic resilience for this sector is crudely assessed. Finally, the progress on the recovery process or adaptive resilience is discussed. Fortunately, most of the main hospitals in Mexico City were not severely damaged, and that it was why most of them and the hospital bed capacity in Mexico City previous to the 2017 earthquake was able to be recovered on time to attend the Covid-19 pandemic which affected Mexico since early 2020.

Accurate damage estimation after earthquakes is crucial for effective post-disaster response and recovery. However, earthquakes often trigger various additional hazards, such as landslides and liquefaction, making accurate building damage estimation even more challenging. To date, despite significant research efforts, automated, accurate building-specific damage estimation has not been achieved. Our study tackles this challenge. We integrate multi-sourced global building footprints and InSAR coherence-based Change Detection Maps (CDMs) generated by the U.S. Geological Survey (USGS) within a variational causal Bayesian network, providing intricate maps of landslides, liquefaction, and building damage. Our key innovations include: 1) a novel masking strategy for the CDMs, derived from low pre-event mean coherence value and high pre-event coherence standard deviation to eliminate noisy signals in InSAR products induced by irrelevant noise sources (steep slopes, soil moisture and vegetation change, open water, etc.), and 2) variational inference to differentiate potential causes of the changes in InSAR coherence signals, specifically landslides, liquefaction, building damage, and non-hazard changes. Our strategy is critical for enhancing the accuracy of building damage and ground failure assessments, as noise from environmental or human-induced changes can obscure true damage signals. We provide reliable damage identification with attribution to specific causes by focusing on accurate building footprints and improving regional ground failure predictions using the 2023 M6.8 Morocco earthquake to validate our methodology. Our approach enables thorough damage analysis across numerous buildings, with the potential for significantly aiding disaster management and marking a substantial advancement of post-earthquake building damage assessment methods.

T & uuml;rkiye is a geographical feature with intense seismic activity due to its tectonic features. Despite such a high earthquake risk, the evaluation of parameters affecting earthquake damage is still very inadequate in T & uuml;rkiye. The aim of this study was to evaluate the parameters affecting earthquake damage in the 6 February 2023 Kahramanmaras earthquake, which caused the highest number of casualties in the history of the Republic of T & uuml;rkiye. Therefore, data were produced to understand the differences in the behavior of structures in the case of an earthquake hazard in different parts of T & uuml;rkiye. The study used sample data from 198,634 buildings with varying types of structural damage in residential areas where the earthquake had been felt. The relationship between these data and key factors causing structural damage was analyzed using a Geographic Information Systems (GIS)-based Random Forests (RF) Machine Learning (ML) model. As a result of this study, it was understood that the 6 February 2023 Kahramanmaras earthquakes caused structural damage as a result of different combinations of building age, local soil conditions, distance to fault lines, distance to the epicenter, ground slip velocity, maximum ground velocity, and soil liquefaction effect factors