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    Prediction of Liquefaction Induced Lateral Spreading Displacements by Artificial Intelligence Based Model
    (Amer Soc Civil Engineers, 2023) Ozener, Pelin; Cetinkaya, Okan
    In recent years, many researchers developed different methods for the estimation of liquefaction-induced lateral spreading displacements that caused major damage during earthquakes. While some of the studies are based on simplified analytical methods, some are based on artificial intelligence applications that are effective in solving many important and complicated problems. Within the scope of this study, an estimation method based on artificial intelligence is used to predict the liquefaction induced lateral spreading by using a comprehensive data set. For this purpose, a more up-to-date data set is created by adding the data of 2010 Darfield and 2011 Christchurch earthquakes to the existing data sets produced with standard penetration test data used in the past. Additionally, since majority of the prediction models consider the thickness of the liquefied layer, fine grain content, average grain diameter, earthquake magnitude, maximum ground acceleration, and distance to the seismic source as the most important parameters on liquefaction-induced lateral displacements, the data set used in this study is improved by involving the velocity-based intensity measures. As a result of the study, the relative importance of each selected velocity-based intensity measure on the lateral spreading displacements is examined and compared with each other in order to reveal their capabilities for the calculation of liquefaction-induced lateral displacements.