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<p>Skirted foundations are usually used in marine engineering. More researches revealed that the variations in soil undrained shear strength considerably influence the assessing performance of the bearing capacity of skirted foundations. This study proposes two machine learning-based methods to predict safety factors (<i>F</i><sub>s</sub>) of skirted foundations under combined loadings. By comparing the prediction performance of models based on Convolutional Neural Networks (CNN) and Gaussian Process Regression, this study investigates the effect of input size of soil random field on prediction accuracy and identifies the optimal CNN model. The proposed CNN model efficiently predicts corresponding safety factors for different combined loadings under various soil random fields, achieving similar accuracy to the traditional time-consuming random finite element. Specifically, the coefficient of correlation exceeds 0.93 and the mean relative error is less than 2.8% for the variation of the horizontal scales of fluctuation under different combined loadings. The relative error of the predicted <i>F</i><sub>s</sub> value is less than 3.00% given three failure probabilities considering the variation of the vertical scales of fluctuations. These results demonstrate satisfactory prediction performance of the proposed CNN model.</p>

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Prediction of skirted foundation safety factors under combined loading in spatially variable soils using machine learning

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Haifeng Cheng,
  • Yongxin Wu,
  • Houle Zhang,
  • Zihan Liu,
  • Yizhen Guo,
  • Yufeng Gao

摘要

Skirted foundations are usually used in marine engineering. More researches revealed that the variations in soil undrained shear strength considerably influence the assessing performance of the bearing capacity of skirted foundations. This study proposes two machine learning-based methods to predict safety factors (Fs) of skirted foundations under combined loadings. By comparing the prediction performance of models based on Convolutional Neural Networks (CNN) and Gaussian Process Regression, this study investigates the effect of input size of soil random field on prediction accuracy and identifies the optimal CNN model. The proposed CNN model efficiently predicts corresponding safety factors for different combined loadings under various soil random fields, achieving similar accuracy to the traditional time-consuming random finite element. Specifically, the coefficient of correlation exceeds 0.93 and the mean relative error is less than 2.8% for the variation of the horizontal scales of fluctuation under different combined loadings. The relative error of the predicted Fs value is less than 3.00% given three failure probabilities considering the variation of the vertical scales of fluctuations. These results demonstrate satisfactory prediction performance of the proposed CNN model.