Numerical study and load and resistance factor design (LRFD) calibration for reinforced soil retaining walls.

摘要

Load and resistance factor design (LRFD) (often called limit states design (LSD)) has been mandated in the AASHTO Bridge Design Specifications and will be adopted in future editions of Canadian Highway Bridge Design Code for all transportation-related structures including reinforced soil retaining walls. The ultimate objective of this thesis work was to carry out reliability-based analysis for load and resistance factor design calibration for rupture and pullout limit states for steel and geosynthetic reinforced soil walls under self-weight and permanent surcharge loading conditions. In order to meet this objective it was necessary to generate large databases of measured load and resistance data from many sources and in some cases to propose new design models that improve the accuracy of underlying deterministic load and resistance models. Numerical models were also developed to model reinforced soil wall performance. These models were used to investigate load prediction accuracy of current analytical reinforcement load models. An important feature of the calibration method adopted in this study is the use of bias statistics to account for prediction accuracy of the underlying deterministic models for load and resistance calculations, random variability in input parameter values, spatial variation and quality of data. In this thesis, bias is defined as the ratio of measured to predicted value. The most important end product of the work described in this thesis is tabulated resistance factors for rupture and pullout limit states for the internal stability of steel and geosynthetic reinforced soil walls. These factors are developed for geosynthetic reinforced soil wall design using the current AASHTO Simplified Method, a new modified Simplified Method, and the recently proposed K-Stiffness Method. Useful quantitative comparisons are made between these three methods by introducing the concept of computed operational factors of safety. This allows designers to quantify the actual margin of safety using different design approaches.