Management of Uncertainty for Flexible Pavement Design Utilizing Analytical and Probabilistic Methods

Abstract

This dissertation develops a systematic and comprehensive approach to management of uncertainty by accomplishing four major objectives: address model uncertainty for the permanent deformation model, develop a method to reduce computational expense, design a framework for incorporation of uncertainty in pavement design, and demonstrate a framework for risk-based mechanistic-empirical (M-E) pavement design. Pavement analysis is impacted by uncertainty from a number of significant sources such as field variables, uncertainty in the predicted behavior models, and errors in these models. These sources of uncertainty are not currently accounted for in the pavement design process in an efficient and comprehensive way. Management of uncertainty for M-E pavement design requires quantification of model uncertainty and the permanent deformation model is significantly susceptible to model uncertainty. A thorough calibration process is performed in this dissertation on three prevalent permanent deformation models: a model incorporating shear theory, an axial strain model, and a model combining both mechanistic theories. Understanding the uncertainty associated with these permanent deformation models is necessary to accurately predict pavement performance, but current models are computationally expensive. A surrogate model is constructed that accurately emulates the M-E flexible pavement design equations. The third major objective develops a logical and efficient process for incorporating uncertainty into M-E pavement design. Propagation of uncertainty from input variability, M-E prediction models, and the surrogate model is demonstrated and a sensitivity analysis is performed. Analysis of methods for the selection of the quantity and location of training points for the surrogate model is presented. Reliability analysis is performed utilizing probabilistic and analytical methods. A method for developing load and resistance factors is presented as a practice-ready option for reliable pavement design. The final major objective presents a framework for risk-based design in the context of mechanistic-empirical pavement design. Through these four major objectives, this dissertation presents a comprehensive framework for management of uncertainty in flexible pavement design.