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<p>This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels (no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network (SNN) and deep neural network (DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value, applied load, installation type, and soil configuration (loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering, addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving <i>R</i><sup>2</sup> values ranging from 0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with <i>R</i><sup>2</sup> values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures.</p>

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Enhanced Load-Settlement Curve Forecasts for Open-Ended Pipe Piles Incorporating Soil Plug Constraints Using Shallow and Deep Neural Networks

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Luttfi A. Al-Haddad,
  • Mohammed Y. Fattah,
  • Wissam H. S. Al-Soudani,
  • Sinan A. Al-Haddad,
  • Alaa Abdulhady Jaber

摘要

This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels (no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network (SNN) and deep neural network (DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value, applied load, installation type, and soil configuration (loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering, addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving R2 values ranging from 0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with R2 values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures.