Comparative Analysis of Machine Learning and Multi-View Learning for Predicting Peak Penetration Resistance of Spudcans: A Study Using Centrifuge Test Data

Punch-through accidents pose a significant risk during the positioning of jack-up rigs. To mitigate this hazard, accurate prediction of the peak penetration resistance of spudcan foundations is essential for developing safe operational plans. Advances in artificial intelligence have spurred the widespread application of machine learning (ML) to geotechnical engineering. To evaluate the prediction effect of different algorithm frameworks on the peak resistance of spudcans, this study evaluates the feasibility of ML and multi-view learning (MVL) methods using existing centrifuge test data. Six ML models—Random Forest, Support Vector Machine (with Gauss, second-degree, and third-degree polynomial kernels), Multiple Linear Regression, and Neural Networks—alongside a Ridge Regression-based MVL method are employed. The performance of these models is rigorously assessed through training and testing across various working conditions. The results indicate that well-trained ML and MVL models achieve accurate predictions for both sand-over-clay and three-layer clay strata. For the sand-over-clay stratum, the mean relative error (MRE) across the 58-case dataset is approximately 15%. The Neural Network and MVL method demonstrate the highest accuracy. This study provides a viable and effective empirical solution for predicting spudcan peak resistance and offers practical guidance for algorithm selection in different stratigraphic conditions, ultimately supporting enhanced safety planning for jack-up rig operations.