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.

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical infrastructures. Therefore, effective prediction of pipe displacement is crucial for preventive management strategies. This study aims to develop a fast, hybrid model for predicting vertical displacement of pipe networks when they experience faulting. In this study, the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network (ANN) to generate predictions the behavior of the soil when different parameters of it are changed. For this purpose, a finite element model is developed for a pipeline subjected to normal fault displacements. The data bank used for training the ANN includes all the critical soil parameters (cohesion, internal friction angle, Young's modulus, and faulting). Furthermore, a mathematical formula is presented, based on biases and weights of the ANN model. Experimental results show that the maximum error of the presented formula is 2.03%, which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence, helps to optimize the upcoming pipeline projects.