B164

The ability to manage risk in geotechnical engineering relies on a realistic assessment of the probability of failure for designs. Most reliability analyses focus on the mean and variance and an assumed, mathematically convenient distribution to model the left-hand tail of the distribution for capacity. However, the reliability of a geotechnical engineering system is governed by a physical constraint on the smallest available capacity. This lower-bound capacity is usually neglected in conventional reliability analyses.

In this study, databases of load tests conducted on offshore and onshore deep foundations are analyzed to provide evidence for the existence of a lower-bound capacity that can be calculated using site-specific soil properties and information about the geometry of the foundation. Next, realistic probability distributions that can accommodate a lower-bound capacity are proposed and used to relate reliability to the lower-bound capacity. Multiple Load and Resistance Factor Design (LFRD) design-checking formats that include information on the lower-bound capacity in addition to the conventional design information are then introduced. Finally, practical approaches are presented for updating information about lower-bound capacities using installation data, proof-load data, and historical performance of foundations under load.

Databases with deep foundations show clear evidence of the existence of a lower-bound capacity that typically ranges from 0.4 to 0.8 of the predicted capacity in both cohesive and cohesionless soils. Results from reliability analyses indicate that the presence of a lower-bound capacity can have a significant affect on increasing the reliability of a deep foundation. The effect of the lower-bound capacity increases as the coefficient of variation for the capacity increases and as the target reliability index increases. This result indicates that reliability-based design codes need to incorporate information about lower-bound capacities. Incorporation of the lower-bound capacity into design is expected to provide a more realistic quantification of reliability for decision-making purposes and therefore a more rational basis for design.