Hybrid FEM-MLP Approach for Evaluating the Ultimate Load Capacity of Bored Piles in Sandy Clay Soil

This study developed a machine learning model based on a multi-layer feedforward neural network to predict the settlement and ultimate load capacity of bored piles using data from three-dimensional finite element simulations. A finite element model was established for sandy clay soil with elastic modulus ranging from 14,000 to 26,500 kN/m², internal friction angle from 28° to 34°, and cohesion from 3 to 6 kN/m². A bored pile with a diameter of 800 mm, a length of 43 m, an elastic modulus of 34.5 × 10⁶ kN/m², and a unit weight of 25 kN/m³ was analyzed under incremental static loading. The simulation results were used to train a neural network with three hidden layers (64–64–32 neurons) and a ReLU activation function. The model achieved high predictive accuracy, with R² = 0.9983, MAE = 6.60 mm, and RMSE = 11.43 mm, indicating effective representation of the nonlinear load–settlement relationship. The ultimate load capacity was estimated using piecewise linear regression, curvature-based analysis, and the slope ratio method, yielding values between 14,400 and 15,900 kN/m², with deviations of less than 10% compared to field static load test results. The results indicate that the proposed FEM–MLP framework can supplement finite element analyses.

• Record URL:
  • https://doi.org/10.1007/s40515-026-00813-x
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21967202
• Supplemental Notes:
  • © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
• Authors:
  • Nhat, Luan Vo
  • Anh, Tuan Nguyen
  • Van, Hoa Tran Vu
• Publication Date: 2026-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 32
• Serial:
  • Transportation Infrastructure Geotechnology
  • Volume: 13
  • Issue Number: 2
  • Publisher: Springer Publishing
  • ISSN: 2196-7202
  • EISSN: 2196-7210
  • Serial URL: http://link.springer.com/journal/40515

Subject/Index Terms

• TRT Terms: Bearing capacity; Clay soils; Finite element method; Geotechnical engineering; Machine learning; Neural networks; Predictive models; Sandy clays; Support piles
• Subject Areas: Bridges and other structures; Geotechnology; Transportation (General);

Filing Info

• Accession Number: 01979846
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 18 2026 12:00PM