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Geomechanics and Engineering
  Volume 37, Number 4, May25 2024 , pages 395-418

Assessment of maximum liquefaction distance using soft computing approaches
Kishan Kumar, Pijush Samui and Shiva S. Choudhary

    The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (Re), maximum fault distance (Rf), or maximum hypocentral distance (Rh), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (Re, Rf, or Rh) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.
Key Words
    earthquake; epicentral distance; extreme gradient boosting; liquefaction; machine learning algorithms; regression
Kishan Kumar, Pijush Samui and Shiva S. Choudhary: Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar 800005, India

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