Application of the neural network model in predicting the threedimensional response of suction caissons on clay
Xilin Yin (Student TU Delft)
Huan Wang (Norwegian Geotechnical Institute, TU Delft - Geo-engineering)
F. Pisanò (TU Delft - Geo-engineering)
K.G. Gavin (TU Delft - Geo-engineering)
Amin Askarinejad (TU Delft - Geo-engineering)
Hongpeng Zhou (The University of Manchester)
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Abstract
Predicting the nonlinear load response of caisson foundations is critical to the foundation design. Despite extensive studies aimed at developing models for predicting the combined V-H-M bearing capacity of suction caissons in clay, accurately predicting the three-dimensional (3D) deflection response of the foundation remains a significant challenge. In this paper, we present a novel solution by developing a fully connected (FC) neural network model that enables load-deflection prediction of suction caissons on clay. To train and evaluate the FC model, a series of 3D finite element simulations were performed covering caissons responses with an embedment ratio of up to 1. The effect of various model hyperparameters on the model's prediction accuracy and generalisation ability was systematically investigated. The results show that the proposed model achieves load-deflection response prediction with simplicity, efficiency and accuracy, demonstrating the significant potential of deep learning technology in the geotechnical design of foundations.