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Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques

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Abstract

Landslides refer to a wide range of processes that result in the downward and outward movement of slope-forming materials, which may spread. Estimating lateral spreading of soil is essential because of the complexities associated with the lateral spreading behavior. Existing empirical models for predicting liquefaction-induced lateral spread displacement are developed using a dataset that varied in terms of earthquake magnitude, source distance, ground slope, layer thickness, fines content, and grain size. The aim of this study is to increase the accuracy of earthquake-induced lateral spreading prediction using multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) model. MGGP, MLP, and RF model predictions of lateral spreading are compared with the results anticipated using machine learning techniques and conventional approaches. Results showed that the MGGP outperforms the Hamada, Youd, MLP, and RF equations for estimating maximum lateral displacement under free-face and gently sloping ground conditions according to the comparisons. The MGGP, which is proved to be better, was also utilized to estimate total lateral displacement for Adapazari data, along with machine learning techniques and conventional approaches.

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Authors and Affiliations

  1. Department of Civil Engineering, Erciyes University, Kayseri, Turkey

    Zulkuf Kaya, Levent Latifoglu, Erdal Uncuoglu & Aykut Erol

  2. Department of Civil Engineering, Dicle University, Diyarbakir, Turkey

    Mehmet Salih Keskin

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  1. Zulkuf Kaya
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  2. Levent Latifoglu
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  4. Aykut Erol
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  5. Mehmet Salih Keskin
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Correspondence to Erdal Uncuoglu.

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Kaya, Z., Latifoglu, L., Uncuoglu, E. et al. Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques. Bull Eng Geol Environ 82, 84 (2023). https://doi.org/10.1007/s10064-023-03103-9

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