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<p>The cyclic stress–strain-strength behavior of sands is strongly influenced by factors such as relative density, confining stress, static shear stress, and soil fabric. To investigate the effects of relative density and static shear stress, constant volume (undrained), stress-controlled cyclic direct simple shear (CDSS) tests were conducted on Ottawa F-65 sand at relative densities of 50%, 60%, and 90%, complementing earlier tests at 55%, 65%, and 75%. The effect of static shear stress on cyclic resistance was further examined through CDSS tests with initial shear stress ratios of 0, 0.075, 0.15, 0.25, and 0.375. Results showed that higher initial shear stress increases cyclic resistance, with denser specimens exhibiting greater resistance to liquefaction. An Artificial Neural Network (ANN) model was trained on the initial dataset and refined as new test data became available. The ANN accurately predicted cyclic resistance within the range of relative densities and static shear stresses used in training but showed limited accuracy for extrapolated conditions beyond this range.</p>

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Cyclic Resistance of Ottawa F-65 Sand: Experiments & ANN Modeling

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Mahtab Miremadi,
  • Majid T. Manzari

摘要

The cyclic stress–strain-strength behavior of sands is strongly influenced by factors such as relative density, confining stress, static shear stress, and soil fabric. To investigate the effects of relative density and static shear stress, constant volume (undrained), stress-controlled cyclic direct simple shear (CDSS) tests were conducted on Ottawa F-65 sand at relative densities of 50%, 60%, and 90%, complementing earlier tests at 55%, 65%, and 75%. The effect of static shear stress on cyclic resistance was further examined through CDSS tests with initial shear stress ratios of 0, 0.075, 0.15, 0.25, and 0.375. Results showed that higher initial shear stress increases cyclic resistance, with denser specimens exhibiting greater resistance to liquefaction. An Artificial Neural Network (ANN) model was trained on the initial dataset and refined as new test data became available. The ANN accurately predicted cyclic resistance within the range of relative densities and static shear stresses used in training but showed limited accuracy for extrapolated conditions beyond this range.