Quantification of uncertainties in soil parameters and geotechnical models is a prerequisite for a reliability-based design. If there is abundant amount of high quality data that can characterize the adopted geotechnical model and its parameters perfectly, the result of reliability analysis will be a certain value (a fixed reliability index or failure probability). Then, the reliability-based design will be a straightforward process and the least cost design that satisfies the constraint of a target failure probability can be selected as the final design. If uncertainty exists in the statistical characterization of the adopted geotechnical models and their parameters, as is usually encountered in geotechnical practice, then the computed failure probability will not be a fixed value and the design decision will not be as straightforward, as there will be uncertainty as to whether the design actually meets the failure probability requirement. To reduce the effect of uncertainty of the statistical characterization of the adopted geotechnical models and soil parameters, a new geotechnical design approach, called reliability-based robust geotechnical design (RGD) method, is developed. This new design methodology is aimed at achieving a certain level of design robustness, in addition to meeting safety and cost requirements. Here, a design is deemed "robust" if the predicted system response is "insensitive" to the uncertainty of the statistical characterization of soil parameters and model factors. A Pareto Front, which describes a trade-off relationship between cost and robustness at a given safety level, is established through a multi-objective optimization based on the RGD concept. The new design methodology is illustrated with an example of spread foundation design. The significance of this methodology is elaborated and demonstrated in this paper.
All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology