Prediction of undrained failure envelopes of skirted circular foundations using gradient boosting machine algorithm

椭圆 包络线(雷达) 算法 嵌入 Boosting(机器学习) 数学 正交性 航程(航空) 几何学 应用数学
作者
Hongzhen Chen,Zhichao Shen,Le Wang,Chongchong Qi,Yinghui Tian
出处
期刊:Ocean Engineering [Elsevier]
卷期号:258: 111767-111767
标识
DOI:10.1016/j.oceaneng.2022.111767
摘要

Skirted circular foundations have been widely used in offshore engineering and are subjected to combined vertical ( V ), horizontal ( H ) and moment ( M ) loading. Undrained load-carrying capacities of skirted circular foundations under combined V–H-M loading were derived from the finite element limit analysis (FELA) in this paper considering a wide range of embedment ratios and soil strength heterogeneity indices. A double-ellipse fitting strategy was proposed and employed to fit numerical results with a unified failure envelope expression. Due to complex interactions between different variables, gradient boosting machine (GBM) algorithm was introduced to learn the relationship between fitting parameters in the failure envelope expression and their influencing variables based on the database constructed by FELA. The results in this study show that the double-ellipse fitting strategy provides comparably accurate and more conservative predictions of failure envelopes compared with existing fitting strategies. The GBM model developed in this study has a good performance in predicting fitting parameters of failure envelopes. The importance of influencing variables and the effect of database size and data orthogonality on the performance of GBM model were discussed. The method based on double-ellipse fitting strategy and GBM algorithm can be implemented in a program to generate failure envelopes conveniently. • A double-ellipse fitting strategy was proposed for failure envelopes fitting. • Gradient boosting machine (GBM) was used to predict failure envelopes under a wide range of boundary conditions. • The failure envelopes were well predicted and the embedment was the most important influencing variables. • Effect of database size and data orthogonality on the performance of GBM model were discussed.
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