Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance (2019)

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126 CHAPTER 6. SUMMARY CONCLUSIONS This project proposed several enhancements to the Pavement ME Design with the purpose of increasing the sensitivity of pavement performance to base layers and subgrade. These enhancements include: a) ANN models to predict the SWCC by using the soil physical properties and climatic parameters; b) ANN models to predict the stress and moisture-dependent MR; c) a modified modulus of subgrade reaction (k) model that considered the cross anisotropy of base material and the shear interaction between the PCC slab and the base course; d) a new faulting model for predicting the faulting depth at joints along the wheel path in jointed concrete pavements over time by using the LTPP data; e) subroutines written in the C# language to implement all of the enhancements described above (Appendix I to Appendix M); and f) characteristic equations that relate the equilibrium soil moisture suction beneath a pavement to the TMI for each of the AASHTO Soil Classes of base course and subgrade soils. This last item is essential to make the Pavement ME Design sensitive to moisture throughout the United States. The present version of Pavement Design accounts for the presence of moisture in the pavement layers only in those cases where the water table elevation is within approximately 33 ft (10 m) of the pavement surface. When the water table is deeper than 10 m as is usually the case in large portions of the United States, the pF beneath the pavement is controlled by the long-term local climatic moisture balance between rainfall and evapotranspiration from the soil and vegetation. The TMI provides a numerical determination of the local moisture balance. The pF that develops at depths below the soil surface also depends upon the soil water retention capacities of the subgrade soil in the form of its SWCC. Using the relationship between the TMI and the SWCC of local soils and the soil map of the United States that is provided by USDA-NRCS, researchers have developed equations that predict the pF beneath a pavement at any location within the United States. This development permits the determination of the equilibrium soil moisture suction within each layer of a pavement around which the suction with vary seasonally. As noted in Chapter 4, all the properties of the unbound base layer and subgrade beneath a pavement are characterized by their dependence upon the suction and its variations. When it is implemented within the Pavement ME Design software, this development will provide the moisture sensitivity in design, which was one of the major objectives of this project. Soil Water Characteristics Curve of Base and Subgrade for Flexible and Rigid Pavements  Two three-layered neural network architectures consisting of one input layer, one hidden layer, and one output layer are constructed for plastic and non-plastic soil. The input variables for plastic soil include the material percent passing the No. 4 sieve, material percent passing the No. 200 sieve, LL, PI, saturated volumetric water content, and local MAAT. The input variables for non-plastic soil are particle diameter corresponding to 30 percent, 60 percent, and 90 percent passing of material (D30, D60, and D90), ϴ, Ѱ, saturated volumetric water content, and local MAAT. The hidden layer assigns 20 neurons. A total of 3600 plastic soil and 250 non-plastic soil data collected from the NCHRP 9-23A project were used to develop the ANN models.

127  Compared to the existing prediction models, such as the Zapata and Perera models, the developed ANN models have the highest accuracy (e.g., smallest RMSE and highest R2 values) to predict the SWCC fitting parameters in the Fredlund-Xing equation. The developed ANN models can accurately estimate the matric suction of soil at any given saturation level. The obtained R2 values are 0.95 and 0.91 for plastic and non-plastic soils, respectively.  The prediction accuracy of the developed ANN models is validated by two data sources (i.e., test data from the NCHRP 9-23A database and independent data from other literatures). The comparison of model predicted matric suction values to the measured ones validates that the developed ANN models are capable of accurately predicting the SWCCs for both plastic and non-plastic soils. Resilient Modulus of Base and Subgrade for Flexible and Rigid Pavements  Two three-layered ANN models were developed for plastic and non-plastic base materials. Input variables for plastic and non-plastic soil included material percent passing 3/8 in. sieve, material percent passing No. 200 sieve, plastic limit, PI, MDD, OMC, and TMC. The output variables are the three coefficients k1, k2, and k3 of MR model.  The developed ANN models showed a higher prediction accuracy compared to the three regression models selected from the existing literature. The ANN models can accurately estimate the MR of base materials at any stress level. The obtained R2 values are 0.91 and 0.90 for plastic and non-plastic base materials, respectively.  The ANN models do not provide any insight into the complex relationship between the MR model coefficients and the base physical properties. Thus, it is not recommended to use as a prediction tool for the values that are out of the range of training data set. Appendix D contains the range of each variable used. In this study, researchers collected a large data set of 779 base materials from the LTPP database and hence provided a wide range of input properties. A sensitivity analysis should be conducted in a future study to evaluate the influence of each input parameter on MR model coefficients.  The developed ANN models were validated using the MR test data from different sources. The R2 value between the measured and predicted validation MR values was 0.8. Modulus of Subgrade Reaction for Rigid Pavements  A slab-base interface shear bonding submodel was developed based on the shear strength properties of the base course, c and  . Depending on the degree of bonding in the slab- base interface, the equivalent section, and the altered deflection basin, which further affected the modified k-value.  The estimated degree of bonding values was compared with the previously developed BBF approach bonding condition. In the BBF approach, the slab-base interface is only considered as a non-bonded or fully bonded condition. However, this study found that most of the treated base layers were either in fully bonded or partially bonded condition and the unbound base layers were mostly partially bonded.  Modified k-values were compared with the BBF k-values. Significant changes in the k- values were observed due to the base modulus and interface bonding corrections.

128 Modified k-values were compared with the BBF k-values. Significant changes in the k-values were observed due to the base modulus and interface bonding corrections. The BBF approach has higher k-value than the modified model due to the difference in interface bonding ratios. The interface bonding between slab and base is considered fully bonded for most of the cases while modified k-value model considered partially bonded condition.  A three-layered ANN model was constructed to predict the modified k-value, which included one input layer, one hidden layer, and an output layer. The FWD deflection basins were computed using FE program. The developed ANN model was validated by comparing the prediction results with the calculated modified k-values from the LTPP pavement sections. The obtained R2 value of 0.92 indicated that the developed models had a desirable accuracy in the prediction of the modified k-value. A sensitivity analysis was conducted to evaluate the effect of the degree of bonding on k-value. The results showed that, in general, a higher degree of bonding produces a higher modified k-value. Faulting of Base Layer for Rigid Pavements  Two faulting prediction models were developed to estimate faulting over time and with axle load distributions. One is to predict the entire faulting development over time. The other is to predict the faulting depth prior to the inflection point in the faulting curve with traffic (axle load distributions).  Field faulting data plot graphically as an S-shape curve with negative curvature prior to the inflection point and positive curvature after the inflection point. The inflection point is the critical point in the development of faulting. Prior to the inflection point faulting is controlled by the permanent deformation in the supporting base course. After the inflection point, water that has infiltrated through the joint in the concrete pavement and filled the void created by the permanent deformation is driven by the passing traffic to scour the surface of the base course. This erosion accelerates the rate of faulting, because the critical inflection point signals the beginning of rapidly deteriorating erosion. The structural design of concrete pavements should use the critical inflection point as a design criterion. Erosion can be controlled if the predicted faulting depth can be kept below the critical faulting depth.  The load-related faulting model depends on different stress states and axle load distributions. The reasonable and consistent stress terms in the model can be determined by using elastic analytical equations. The axle load distributions are determined by the WIM data collected from LTPP data or estimated with AADTT.  Because of its potential importance for jointed concrete pavement design, a separate model was developed to predict the critical faulting depth.  To better implement the proposed models, the coefficients in the models were statistically calibrated with performance-related factors using a stepwise method to select the relevant variables and a generalized linear model to perform multiple regression analysis. All three predictive models fit the field data very well, and the models of the coefficients of the full faulting and load-related faulting models have high R2-values and can be expected to provide exceptionally reliable predictions of the development of faulting with time and with accumulating traffic loads.

129 Prediction of Pavement Performance and Sensitivity Analysis Based on the sensitivity analysis performed on the pavement models and the comparison of models proposed by researchers and the Pavement ME Design Guide, several findings are presented as follows:  As an unbound granular material, the nonlinearity and the anisotropy of the pavement materials should be considered in the pavement design and analysis. With such properties, the modulus of the base and subgrade are sensitive to the moisture content, the loading level, and the pavement structure, which affect the pavement responses and performance.  The proposed model for MR of the base and subgrade eliminates the steps to transfer the modulus for different moisture conditions as in the Pavement ME Design Guide. The modulus can be estimated based on the suction/moisture, depth, stress state, etc.  The proposed rutting model is more involved with the stress state and material properties. The calculated rut depth is greater than the one using the current method in the Pavement ME Design Guide.  The increase of the loading level reduces the load repetitions to the fatigue cracking failure and increases the rut depth in the base layer.  The increase of the asphalt layer thickness increases the load repetitions to the fatigue cracking failure and reduces the rut depth in the base layer.  The increase of the base layer thickness reduces the load repetitions to the fatigue cracking failure and reduces the rut depth in the base layer.  As the moisture content increases in the base layer, the load repetitions to the fatigue cracking failure reduces. The rut depth in the base layer shows an increasing trend with the increasing moisture content.  Some factors can reduce the development of faulting, including the use of dowel in jointed concrete pavement, the selection of stabilized (bound) base course and thicker base course. On the contrary, the freeze-thaw cycle is a favorable impact on the development of faulting. The more freeze-thaw cycles result in larger faulting. This is consistent with the fact that a major cause of faulting is temperature variations. Similarity, the faulting develops faster and greater in the WF climatic zone. Additionally, the greater of days32C results in less faulting but after the critical inflection point, the order of faulting magnitude reverses.  The ANN model for k-value was used to evaluate the sensitivity of moisture and degree of bonding using the proposed models. The sensitivity of modified k-values was found to be improved significantly to moisture and degree of bonding compared to the exiting Pavement ME Design k-values. The sensitivity of moisture increased for all selected pavement sections due to the inclusion of suction effect in the MR model. However, the effect of degree of bonding is quite same for modified k-value and Pavement ME Design k-value when there is either fully bonded or no bond condition in slab-base interface. But the developed ANN model can predict k-value for partially bonded condition as well.  To study the effect of moisture and degree of bonding on pavement performance, tensile stress at top and bottom of slab and differential deflection across the transverse joint were evaluated. The sensitivity of moisture and degree of bonding were calculated using the proposed MR and modified k-value model and compared with the results from Pavement ME Design MR and k-values. The MR values using the proposed model and the

130 corresponding modified k-values from ANN model showed much higher sensitivity on calculated stress and deflections compared to the results from Pavement ME Design models. FUTURE WORK AND RECOMMENDATIONS There are several items of future work that emerge from the work that has been accomplished in this project including the following:  Replace the models that are currently in the Pavement ME Design software by those that have been developed in this project. All these models are currently incorporated in separate subroutines that can be added to the current version of Pavement ME Design software.  Incorporate the TMI- AASHTO Soil Class – Equilibrium Soil Moisture Suction relationships that have been developed in this project into the Pavement ME Design software.  Develop a mechanistic method of predicting the post-critical faulting of jointed concrete pavements (after the inflection point) to include the effects of scour of the surface of the base course by water trapped in the void beneath to concrete slab, which is propelled by the passage of approaching and receding traffic. The model that was developed in this project predicts the faulting in the wheelpath. What is needed in addition is the faulting that develops in the corners and along the edges of a concrete pavement where the curling and warping of the pavement is typically larger than what occurs in the wheelpath.  Develop the properties of stabilized base course materials as they are controlled and affected by the soil moisture suction, and incorporate those relationships into the Pavement ME Design software.  Re-calibrate the prediction equations of the IRI for both the asphalt-surfaced and concrete-surfaced pavements after incorporating the models that have been developed in this project.