Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance (2019) / Chapter Skim
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23 CHAPTER 4. FINDINGS INTRODUCTION This project addressed the issues associated with the sensitivity of pavement performance to base layers and subgrade for flexible and rigid pavements.
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24 Currently, ANN approach becomes a more popular tool for the development of prediction models. Compared to regression models, the main advantage of the ANN approach is that it can capture nonlinear and complex scattered relationships between input and output parameters.
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25 input variables Xi are selected from the SWCC-related material indicators, which will be elaborated in the following section. In general, the development of ANN models includes two critical steps: 1)
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26 where Ii is the input quantity. φ is a positive scaling constant, which controls the steepness between the two asymptotic values 0 and 1.
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27 database used for training of ANN models consists of 3600 samples of plastic soil and 250 samples of non-plastic soil. A statistical analysis was performed to determine the root mean squared error (RMSE)
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30 that the developed ANN models outperform these existing regression models' prediction accuracies. Table 12.
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31 Table 13. Prediction Accuracy of SWCC Fitting Parameter Models.
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32 (a) Zapata Model (b)
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33 (a) Zapata Model (b)
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36 (a) Plastic soil (b)
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37 (a) Plastic Soil (b)
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38 Figure 10. TMI Distribution in United States (121)
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40 The monthly potential evapotranspiration is quantified using Eq.
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41 Finally, the TMI value is calculated following Eq. 4.13 according to Witczak et al.
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42 Figure 14. GIS-Based Equilibrium Suction Map.
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43 (a) AASHTO soil type: A-1 (b)
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44 A linear regression analysis was performed to predict the equilibrium suction value from the TMI and PI.
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46 1. Matric suction hm in Eq.
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47 literature was not sufficient to train an ANN model. Hence the Soil Survey Geographic Database was used for data collection from USDA-NRCS.
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48 Development of ANN Models for MR Model Coefficients The ANN approach is an adaptive information processing technique, which allows to establish the correlations between the input variables Xi and the output variables Yj through the inter-connected neurons (i.e., weight factor, wji)
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49 models used the same sigmoidal transfer function as that for the SWCC ANN models, 80 percent of the data set was used for training and 20 percent of the data set for validation.
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50 The R2 value for k1, k2, and k3 coefficients of non-plastic base and subgrade materials were in the range of 0.51–0.97. Note that a high level of variability was involved in estimated coefficients due to variations in cyclic load triaxial test results and seasonal changes in selected physical properties (147)
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54 MDD, material percent passing No. 4 sieve, material percent passing No.
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55 (a) Plastic base materials (b)
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56 (a) Yau model (b)
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57 Table 16. Input Parameters Collected from Literature for Model Validation.
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58 The k-value can be measured either from a field plate load test conducted on top of the subgrade (150, 151) or correlation with other load bearing capacity tests (e.g., consolidation test, triaxial test, and California Bearing Ratio test)
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59 (anisotropy) of the base moduli.
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60 H R V R Mn M (4.25) where V RM is the resilient modulus in the vertical direction.
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61 Figure 28. Flowchart of Corrected Base Modulus due to Cross Anisotropy.
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62 Figure 29. Illustration of Transformed-Section Method for a Cooperated Concrete Slab and Base Course System.
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65 sequences with a target load of 40 kN (9000 lb)
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67 Unbound Base Layer Similarly, many researchers had analyzed the shear strength test data and identified the influence of several physical properties on the shear strength parameters of unbound aggregate materials. Density and degree of saturation are the two most important parameters affecting the shear strength parameters of unbound base (179)
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68 (b) Figure 33.
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69 The calculated degree of bonding shows better sensitivity to the observed faulting data. The faulting value decreases with a higher degree of bonding in the slab-base interface.
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70 1 j n j ji i i by f w x (4.37) where f is a transfer function, which normally uses a sigmoidal, Gaussian, or threshold functional form.
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72 Table 19. Selected Range of Input Parameters in ANN Training Data Set.
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73 Figure 38. Target and Output k-values for Training, Validation, and Overall Data Sets for 1296 Simulation Cases.
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74 Figure 40. Modified k-values at 0, 0.3, 0.6, and 1 Degree of Bonding for Selected LTPP Pavement Sections.
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75 ta nnc (4.39) where is the shear stress.
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76 Tutumluer et al., and Chow et al.
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77 parameter is the output of the developed model. The cohesive strength parameters were calculated from the collected 432 unconfined compressive strength test samples.
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78 Figure 44. Target and Output c' Values for Training, Validation, and Overall Data Sets for 432 Subgrade Soils.
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79 h is the thickness of the layer. N is the number of traffic repetitions.
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80 Regression Models for Permanent Deformation Model Coefficients for Unbound Base Layers In order to develop the prediction models of the coefficients of Eq. 4.45, researchers used the measurements from the repeated load triaxial test on 108 different types of base materials collected from various sources (74, 189–192)
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82 FAULTING OF BASE LAYER FOR RIGID PAVEMENTS Illustration of Development of Faulting An understanding of the faulting progression is beneficial to develop the faulting prediction model. Moisture infiltration and non-uniform support of base course are essential keys for the development of faulting (193)
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83 Figure 46. Schematically Illustration of the Development of Faulting.
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84 permanent deformation. Beyond this inflection point, faulting accelerates because this infiltrated water being driven by the moving traffic scours the surface of the base course and causes erosion.
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85 depth at which erosion begins. The point of inflection is where the curvature of the faulting depth curve changes from negative to positive, as shown in Figure 46.
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86 Figure 48. Comparison between Measured and Predicted Faulting Depth.
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87 Multiple Regression Analysis The correlation between the model coefficients and the selected performance-related data is investigated using multiple linear regression analysis. The model parameter of was found to be a constant value of 2e4.
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88 Table 20. Results of Multiple Regression Analysis for Coefficients in the First Faulting Model.
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90 ln( ) 0.582841 1.515503 1.55997 1.570976 0.25955 0.013998 0.026763 32 ipf drainage dowel bound basethick wetdays days C (4.60)
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91 Figure 51. Mean Critical Faulting Depth with or without Dowels.
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92 f , , , m , and n are model coefficients. x , y , and z are normal stresses.
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94 Comparison between Measured and Predicted Faulting Based on Permanent Deformation Characterization The validation of the model for the load-related faulting is based on the LTPP field faulting data. First, the field data were collected for validation of the first faulting model to characterize the entire life of faulting and determine the critical faulting depth at the inflection point.
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95 0 429.470561 15.72206 180.18123 196.160669 0 831.66223 23.586441 32 N FI FT days C dowel days C (4.70) 0.028917 0.000628 0.006929 0.016353 0.031231 0.02095 0.007663 0 FI FT DNF WF WNF days C (4.71)
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96 Figure 54 presents the results of the comparison between the coefficients predicted by Eqs.
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