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25-11-2023 | RESEARCH

An extreme learning neural network approach for seismic bearing capacity estimation of planar caissons in nonhomogeneous clays

Authors: Van Qui Lai, Vinay Bhushan Chauhan, Suraparb Keawsawasvong, Kongtawan Sangjinda, Jitesh T. Chavda, Lindung Zalbuin Mase

Published in: Earth Science Informatics | Issue 1/2024

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Abstract

This study employs a two-dimensional plane strain finite element limit analysis method to evaluate the seismic bearing capacity of a planar caisson in anisotropic and non-homogeneous clay. The anisotropic behavior of the clay is simulated using the Anisotropic Undrained Shear (AUS) failure criterion in the finite element limit analysis (FELA). A rigid caisson has a depth (L) and a width (B). A comprehensive parametric analysis is executed to evaluate the non-dimensional seismic bearing capacity factor (N_ce) in terms of the adhesion factor (α), anisotropic strength ratio (r_e), horizontal seismic coefficient (k_h), depth to diameter ratio (L/D), and shear strength gradient ratio (ρB/s_uc0). The relationship between these parameters to the seismic bearing capacity factor is investigated, and the influence of these parameters on the potential failure mechanisms is discussed in detail. Moreover, an equation for predicting the seismic bearing capacity factor is developed through a machine learning regression approach called the Artificial Neural Network (ANN) model, which practitioners can extensively employ in the field. These correlation functions fit well with those obtained from FELA, with a value of R² = 99.43%.

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Title: An extreme learning neural network approach for seismic bearing capacity estimation of planar caissons in nonhomogeneous clays
Authors: Van Qui Lai
Vinay Bhushan Chauhan
Suraparb Keawsawasvong
Kongtawan Sangjinda
Jitesh T. Chavda
Lindung Zalbuin Mase
Publication date: 25-11-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 1/2024
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01175-5

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