Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil
This study employs random adaptive finite element limit analysis (RAFELA) combined with advanced machine learning (ML) techniques to predict the mean limiting pressure factor in contiguous pile walls (CPWs). Two nonlinear regression models, multivariate adaptive regression splines (MARS) and the gro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025005146 |
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| Summary: | This study employs random adaptive finite element limit analysis (RAFELA) combined with advanced machine learning (ML) techniques to predict the mean limiting pressure factor in contiguous pile walls (CPWs). Two nonlinear regression models, multivariate adaptive regression splines (MARS) and the group method of data handling (GMDH), are developed to forecast the mean limiting pressure factor. The models were evaluated using several statistical performance parameters, scatter plots, residual error curves, and eight statistical performance metrics to ensure predictive accuracy and reliability. Based on the statistical analysis and comparison, the proposed MARS model is the most accurate model, with R2 = 0.983 for training and R2 = 0.969 for the testing phase, followed by GMDH (R2 = 0.971 for training and R2 = 0.944 for testing). Comprehensive measures (COM) reveal that the MARS model, with the lowest COM value of 1.006, outperforms the GMDH model (COM = 4.943), demonstrating superior accuracy in predicting the limiting pressure factor (μNran). Feature importance analysis reveals that the output μNran is most influenced by soil variability (CoVc = 0.749), followed by the adhesion factor (α = 0.735) and soil uniformity (Θc = 0.681), whereas the S/D ratio has the lowest impact (0.378). Moreover, the proposed ML models provide user-friendly empirical equations to calculate the mean limiting pressure factor, requiring minimal computational expertise, thus bridging the gap between theoretical stochastic studies and practical field applications. This research enhances geotechnical design efficiency with robust ML solutions, supporting safer and more sustainable excavation practices under complex subsurface conditions. |
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| ISSN: | 2590-1230 |