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Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation.

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Probabilistic analysis for deep foundation design requires careful consideration of parameter uncertainty. This study uses Monte Carlo simulation to calibrate resistance factors for drilled shafts, revealing their dependence on load levels.

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Area of Science:

  • Geotechnical Engineering
  • Structural Analysis
  • Probabilistic Methods

Background:

  • Probabilistic analysis is essential for service limit state (SLS) design of deep foundations.
  • Accurate incorporation of parameter uncertainty is critical for reliable SLS design.

Purpose of the Study:

  • To describe the use of Monte Carlo simulation for probabilistic analyses.
  • To calibrate resistance factors for drilled shafts at SLS.
  • To investigate the influence of load levels and soil variability on resistance factor calibration.

Main Methods:

  • Monte Carlo simulation was employed for probabilistic analyses.
  • Calibration of resistance factors for drilled shafts was performed under SLS conditions.
  • The study analyzed the impact of load combinations, soil strength variability, and target probability of failure.

Main Results:

  • An impossible calibration case was identified due to specific combinations of load, soil variability, and target failure probability.
  • Resistance factors for drilled shafts in shale were determined and found to be load-dependent.
  • Higher load levels resulted in lower resistance factors.

Conclusions:

  • The developed resistance factors for drilled shafts in shale are responsive to load levels.
  • These findings facilitate the transition from allowable stress design to load and resistance factor design for geotechnical engineers.
  • Understanding the load dependency of resistance factors is crucial for accurate deep foundation design.