Probabilistic Calibration of Resistance Factors for Pile Group Considering the Spatial Variability of Soils

Resistance factors for pile groups are typically derived using empirical methods that do not directly account for system redundancy and overlook the correlation between individual piles, which are inherently influenced by the spatial variability of soils. While rigorous three-dimensional random finite difference or random finite element analyses could potentially address these issues, they are constrained by significant computational demands. To address these issues, this study introduces two novel probabilistic methods for calibrating resistance factors for pile groups based on individual load tests. These methods utilize Bayesian and machine learning techniques, respectively.Comparative analyses indicate that the results derived from both approaches exhibit good agreement.

Keywords: Pile group, Resistance factor, Bayesian approach, Machine learning.