DFI Journal - The Journal of the Deep Foundations Institute

Volume 6, Issue 2, November 2012
DOI: 10.1179/dfi.2012.009

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Performance-Based Reliability Design for Deep Foundations Using Monte Carlo Statistical Methods

Fan, H. & Liang, R.


Deep foundation designs for service limit state are still deterministic in the current AASHTO LRFD Specifications. To address this deficiency, a performance-based reliability design methodology is developed using the Monte Carlo statistical techniques. In the proposed methodology, the design criteria are defined in terms of the allowable displacement. The spatial variability of soil parameters is considered in the proposed methodology by modeling soil parameters as random fields. Failure is defined as the event that the induced displacement exceeds the limiting displacement. The probability of failure by Monte Carlo approach is the ratio of the number of unsatisfactory performance events to the sample size. Three numerical examples are given to illustrate the application of the proposed methodology for laterally loaded and axially loaded drilled shafts, respectively. The spatial variability and correlation of soil properties were shown to exert significant influences on the foundation design.

Monte Carlo statistical methods, performance based reliability, lateral load, axial load, drilled shafts