Calibration of resistance factors for gravity retaining walls

ABSTRACT

Geotechnical design codes have been migrating from the traditional allowable stress design towards load and resistance factor design (LRFD). In this paper, calibration of the geotechnical resistance factors for sliding and overturning limit states of gravity retaining walls is performed using a reliability-based method. For each limit state, the LRFD approach is applied to design the gravity retaining wall, and its probability of failure is estimated using the Random Finite Element Method (RFEM). An example gravity retaining wall is analyzed using the described methodology, and the results suggest that for a given resistance factor the sliding failure probability is greater than the overturning failure probability, and a positive correlation between the soil effective internal frictional angle and unit weight random fields significantly reduces the failure probability. For reasonable ranges of the target maximum lifetime failure probability, the required resistance factor for the sliding limit state is close to that for the overturning limit state. In addition, the sliding resistance factor given by the Canadian Highway Bridge Design Code seems slightly unconservative, while the recommended overturning resistance factor is slightly conservative. The current analysis can be used to guide the calibration of these geotechnical resistance factors used in design codes.

KEYWORDS:

• Reliability analysis
• gravity retaining wall
• resistance factor
• sliding failure mode
• overturning failure mode

Acknowledgement

The authors would like to acknowledge the financial support provided by the Canadian Standard Association Group Research Project Award and Mitacs (grant number: IT21047), and the Natural Sciences and Engineering Research Council of Canada.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding details

This work was supported by the Canadian Standard Association Group Research Project Award and Mitacs under Grant number IT21047; and the Natural Sciences and Engineering Research Council of Canada.