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18 CHAPTER 3. RESEARCH PLAN The research plan requires the development and synthesis of several components: (a) evaluation and screening of unbound layer and subgrade models; (b) development of soil water characteristics curve models of base and subgrade; (c) development of MR models of base and subgrade; (d) development of modulus of subgrade reaction model; (e) development of faulting model of base layers; and (f) conduct of performance prediction and sensitivity analysis. There are four principal purposes of this research plan. The first purpose is to explain and illustrate the details of how the material properties will be computed by the proposed new models and how the model coefficients will be extracted from existing databases. The second purpose is to explain how these material properties when used in the modified Pavement ME Design will be calibrated and validated with the observed performance data on in-service pavements. The third purpose is to explain how the products will be incorporated into the current Pavement ME Design software. The fourth purpose is to demonstrate the higher degree of sensitivity of the predicted pavement performance to these new models. Given the way that the Pavement ME Design is currently structured, the products of the research plan will provide these new material properties as inputs to the Pavement ME Design. EVALUATION AND SCREENING OF UNBOUND LAYER AND SUBGRADE MODELS Based on the synthesis of current knowledge in Chapter 2, researchers propose improvement or alternative models of unbound layers and subgrade for the Pavement ME Design software. These models serve as the potential enhancements to Pavement ME Design to improve influence of the underlying layers on pavement performance. To identify the best solutions from the sources collected, researchers first established evaluation criteria to screen the unbound layer and subgrade models collected in the literature review, and then developed a scoring method to rank the models according to the evaluation criteria. Finally, alternative models were proposed for Pavement ME Design based on the results of the scoring method. Researchers proposed a set of three criteria in the evaluation of unbound layer/subgrade models: 1) susceptibility criterion; 2) accuracy criterion; and 3) development criterion. More details on the criteria and the scoring of each model are presented in Appendix C. The identified potential enhancements are briefly introduced next, which are elaborated in Chapter 4. SOIL WATER CHARACTERISTICS CURVE OF BASE AND SUBGRADE FOR FLEXIBLE AND RIGID PAVEMENTS Researchers improved the prediction accuracy of SWCC using an artificial neural network (ANN) approach. Two three-layer ANN models were constructed for plastic and non- plastic soils separately, which consisted of one input layer, one hidden layer, and one output layer. The input variables included soil gradation indicators, particle diameter indicators, Atterberg limits, saturated volumetric water content, and climatic factors. The hidden layer including a total of 20 neurons used a log-sigmoidal function as a transfer function and the Levenberg-Marquardt back propagation method as the training algorithm. The output layer variables were the fitting parameters of the Fredlund-Xing equation. The SWCC database from the NCHRP 9-23A project was used to develop ANN models with 80 percent of the data set for training and 20 percent of the data set for validation. The developed ANN models have coefficient of determination (R2) values between 0.91 and 0.95 for predicting the SWCCs of unbound material, which are significantly higher than the regression models. Finally, the
19 developed ANN models were validated by comparing a new data set collected from both the NCHRP 9-23A project and other literature sources to the model predictions. EQUILIBRIUM SUCTION OF BASE AND SUBGRADE FOR FLEXIBLE AND RIGID PAVEMENTS Researchers developed a correlation model to the predict equilibrium suction in base and subgrade layers based on Thornthwaite moisture index (TMI). TMI is a climatic parameter that characterizes the annual moisture balance of a specified location based on precipitation, evaporation, water storage, deficit, and runoff. A TMI contour map of continental United States was generated in geographic information system (GIS) platform to determine the moisture index for each specific mapunit collected from the Natural Resources Conservation Service (NRCS) database. Similarly, pF of all mapunits were determined using a fundamental approach of oscillating moisture transient equation proposed by Mitchell (93). Finally, a prediction equation was developed to predict pF from TMI. It showed a good prediction accuracy and had a relationship with the plasticity index (PI) of soil. RESILIENT MODULUS OF BASE AND SUBGRADE FOR FLEXIBLE AND RIGID PAVEMENTS Researchers developed a moisture and stress-dependent model to precisely estimate MR of unbound base materials in unsaturated conditions, and a set of ANN models was developed to predict the coefficients of this model from base physical properties. The developed ANN models consist of seven input variables, 10 hidden neurons, and one output variable. A data set of 717 unbound base materials was collected from the Long Term Pavement Performance (LTPP) database and used to train and generalize the network. Soil physical properties such as gradation (percent passing No. 3/8 sieve, percent passing No. 200 sieve), gradation shape parameter (Ñ°) and gradation scale parameter (Ï´), index properties (i.e., plastic limit (PL) and PI), maximum dry density (MDD), optimum moisture content (OMC), and test moisture content (TMC) were selected as inputs for the ANN model. The MR values estimated using the predicted coefficients were compared with experimental data and showed a R2 value above 0.9, which is much higher than the MR values computed using regression models. Finally, the MR test results from different sources were used to validate the developed ANN models. MODULUS OF SUBGRADE REACTION FOR RIGID PAVEMENTS Researchers developed a modified k-value model to take account of the shear interaction between the Portland Cement Concrete (PCC) slab and the base course. Formulation of the modified k-value model contained four steps: (a) correction of the base modulus due to cross- anisotropy; (b) development of a submodel for slab-base equivalent thickness; (c) development of a formula for interface shear bonding between the slab and base; and (d) determination of the k-value using the calculated shear bonding and the deflection patterns of falling weight deflectometer (FWD). Many rigid pavement structural and strength properties and the corresponding FWD deflection patterns data were collected from the LTPP database and used to calculate the modified k-value. These were compared against the k-values using the previously developed backcalculated best-fit approach. The results showed that the modified k-values changed significantly due to the consideration of cross-anisotropy and slab-base interface bonding in the proposed model. Finally, an ANN approach was employed to predict the modified k-value for various pavement structures, layer moduli, and interface bonding ratios. The FWD
20 deflection pattern for each combination was determined from the finite element (FE) analysis. The prediction accuracy of the ANN model was also examined by comparing the prediction results with the calculated modified k-values for the LTPP pavement structures. The comparison results indicated that the ANN model accurately predicts the modified k-values for the given pavement structures, layer moduli, and interface bonding ratios. SHEAR STRENGTH OF BASE AND SUBGRADE LAYERS FOR FLEXIBLE AND RIGID PAVEMENTS Shear strength of base and subgrade materials is considered to be a dominant factor in pavement performance such as total rutting and erosion. The general shear strength model that is defined according to Mohr-Coulomb failure envelope, does not take into account the impact of moisture variation. Therefore, a moisture-sensitive shear strength model is adopted in this study and regression models have been developed for the prediction of model coefficients (câ and Ïâ). To do so the research team has collected shear strength test data from LTPP and other literature sources. Soil physical and strength properties were also collected from the same sources and two different sets of prediction models were developed for base and subgrade materials. PERMANENT DEFORMATION OF BASE AND SUBGRADE LAYERS FOR FLEXIBLE AND RIGID PAVEMENTS A new ME permanent deformation model was proposed by the research team for base and subgrade layers. The advantage of the new model over the Pavement ME Design model is that it is capable of predicting the permanent deformation behavior at different shear strength conditions. However, the predictability of this model depends largely on the accuracy of the coefficients of the model. In this study, repeated and monotonic load triaxial test data were collected from the literature and the coefficients of permeant deformation were calculated accordingly. Soil physical properties such as gradation, Atterberg limits, dry density and moisture content data were collected for base and subgrade materials and regression analysis was conducted to predict the permanent deformation model coefficients i.e., ϵ0, Ï, β, m and n. FAULTING OF BASE FOR RIGID PAVEMENTS A novel ME model was developed to estimate faulting over time. Two stages of the process of faulting are revealed by the model. To distinguish the two states of faulting, an inflection point can be directly determined by this model and can indicate the beginning of erosion for the concrete pavement design. In addition, the faulting depth before the inflection point is a critical depth due to the permanent deformation of underlying layers. Thus, this first phase of faulting is principally an effect of the permanent deformation of the supporting base course. This second ME faulting model predicts faulting before reaching the critical depth using axle load distributions. In summary, two faulting prediction models were developed. One is to predict the entire faulting development over time. This model of the full faulting curve shows that there is an inflection point in the faulting curve. Before reaching the inflection point, the accumulation of faulting is caused by the permanent deformation of the supporting layers. After passing the inflection point, faulting accelerates due to the action of erosion. The second model is to predict the faulting depth before inflection point with traffic. The proposed models were proven to be considerably accurate and reliable by using LTPP data. The coefficients in the models are statistically calibrated with performance-related factors using multiple regression analysis.
21 PREDICTION OF PAVEMENT PERFORMANCE AND SENSITIVITY ANALYSIS To illustrate the sensitivity of the proposed models as mentioned above, researchers performed a comprehensive sensitivity analysis for both flexible and rigid pavements. For flexible pavements, the proposed SWCC model, MR model, permanent deformation model, and shear strength model are implemented in the FE of pavement structures. The pavement performance evaluated include fatigue cracking, top-down cracking, and permanent deformation. The sensitivity of the pavement responses and performance to different loading levels, climate conditions, and materials are demonstrated. In addition, the predicted performance is compared with that computed by the models in the Pavement ME Design. For rigid pavements, the proposed faulting models are implemented for a selected LTPP pavement section. The sensitivity of the proposed faulting models to the use of dowels, type of base layer, thickness of base layer, and climate conditions is evaluated and presented.
