Implementation of Bayesian theory on LRFD of axially loaded driven piles

Abstract

In this paper, a framework based on Bayesian theory and proof pile load test results was used to update resistance factors of axially loaded driven piles. Prior to implementation of the framework, resistance factors were calibrated based on the distribution of the measured-to-predicted pile ultimate bearing capacity using the results of static pile load tests conducted to failure. These resistance factors and the distribution were considered to be “prior.” The prior distribution of the measured-to-predicted ultimate bearing capacity was updated based on Bayesian theory to incorporate additional proof pile load test results. Using the measured-to-predicted load distributions and the updated (or posterior) measured-to-predicted bearing capacity distributions, resistance factors were calibrated (or updated) from the first-order reliability method (FORM) for two different target reliability indices, 2.33 and 3.0. This research attempted to use the results of proof pile load tests, which are generally conducted to verify pile designs, to update resistance factors. The updated resistance factors varied substantially depending on the proof pile load test results. Therefore, the Bayesian implementation can contribute to economical pile designs.