Two disastrous earthquakes, named Pazarc & imath;k (Mw7.8) and Ekin & ouml;z & uuml; (Mw7.6), occurred on February 6, 2023 in the southeast part of T & uuml;rkiye and were collectively named Kahramanmara & scedil; earthquakes. These seismic events were caused by a left lateral strike-slip faults, and resulted in significant loss of life, severe damage to infrastructures and buildings, and geotechnical damages such as mainly large-scale slope failures, rockfalls, and ground liquefaction. The main goal of this study is to assess the extend and impact of widespread ground liquefaction, particularly on built environment. Additionally, the ranges of amount of settlement and tilting of buildings due to ground liquefaction were briefly discussed and liquefaction caused by Kahramanmara & scedil; earthquakes were compared with those others occurred in T & uuml;rkiye. The site observations indicated that except a village, a short of a highway, a few bridges and two settlements, widespread liquefaction was mainly observed in agricultural non-urbanized fields. The maximum amount of settlement at some liquefaction locations reached up to 2 m and high-raise buildings tilted 7-8 degrees from the vertical reaching up about 20 degrees. Observations indicated that single-storey and two-storeys buildings with a basement to a certain depth, a lower center of gravity and raft foundation should be considered suitable on soils susceptible to liquefaction in earthquake-prone regions without taking any counter-measures against ground liquefaction. Mass movements along the shoreline of the G & ouml;lba & scedil;& imath; Lake were unlikely to be caused by lateral spreading resulting from ground liquefaction and they were rather due to planar sliding along a weak layer dipping towards the lake with progressive failure.

The February 6, 2023 Kahramanmaras,-T & uuml;rkiye ,-T & uuml;rkiye earthquakes with moment magnitudes 7.7 and 7.6 resulted in substantial casualties, injuries and extensive infrastructure devastation. Soil liquefaction was identified as one of the contributing factors to the damages. In this study, a data-driven approach to assess liquefaction-prone areas within an artificial neural network (MultiLayer Perceptron- MLP) was proposed. The study area, selected to cover a region with the size of 11,500 km2 2 containing Amik and Kahramanmaras, , Plains, is governed mainly by active tectonism of the East Anatolian Fault Zone. The earthquakes were considered to be responsible for numerous liquefaction occurrences in both plains. Here, a comprehensive inventory of liquefied regions was compiled from aerial photogrammetric images taken in the days following the earthquakes. Considering the availability of suitable geospatial datasets, the key factors for liquefaction modeling were selected as distance to streams, land use and land cover, slope, and topographic wetness index, and normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from satellite images taken a few days before the earthquakes. The Holocene unit was used as a mask to perform modeling and prediction within this litho- logical type. The F1-score and overall accuracy values obtained from the MLP on a test dataset were 80% and 82%, respectively. The study showed that geospatial databases including airborne and satellite image products have great potential for assessing liquefaction hazard at regional scale, which can be used as base data for planning and conducting further field and laboratory studies to improve the accuracy in predictions.