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

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3 CHAPTER 2. SYNTHESIS OF CURRENT KNOWLEDGE This chapter presents the results of the literature review conducted on the causes of the problems pointed out in Chapter 1 and current solutions that target the identified causes. The answers to these two questions are organized as follows:  Review the characteristics of unbound layers and subgrade used in the AASHTOWare Pavement ME Design software and MEPDG.  Collect and review the influence of unbound layer and subgrade characteristics on the performance of flexible and rigid pavements.  Collect and identify ME models of unbound layers and subgrade that address such influence. CHARACTERISTICS OF UNBOUND LAYERS AND SUBGRADE USED IN PAVEMENT ME DESIGN The AASHTOWare Pavement ME Design software and MEPDG are practical tools for the pavement design and analysis based on ME principles. They predict multiple performance indicators for flexible and rigid pavements, including the following (1):  Flexible pavements: o Total rut depth of the asphalt layers, aggregate base, and subgrade. o Load-related cracking (alligator cracking and longitudinal cracking). o Thermal cracking. o Smoothness (International Roughness Index [IRI]).  Rigid pavements: o Transverse cracking in jointed plain concrete pavement (JPCP). o Faulting in JPCP. o Punchouts in continuously reinforced concrete pavement (CRCP). o Crack spacing and crack width in CRCP. o Smoothness (IRI) in JPCP and CRCP. For each performance indicator above, Pavement ME Design has a distress prediction model that requires inputs from different layers in a pavement structure. Table 1 shows the inputs from unbound layers and subgrade to account for the influence of these underlying layers.

4 Table 1. Inputs from Unbound Layers and Subgrade in Pavement ME Design. Performance Indicator Input Parameters Unbound Layers Subgrade Fl ex ib le P av em en t Total Rut Depth MR Thickness Poisson’s ratio Soil water characteristic curve (SWCC) MR Percent passing No. 200 SWCC Poisson’s ratio Load-related Cracking (alligator and longitudinal cracking) MR Thickness Poisson’s ratio SWCC MR Liquid limit Percent passing No. 200 Poisson’s ratio Groundwater depth SWCC Plasticity index Thermal Cracking MR Thickness Poisson’s ratio SWCC MR Percent passing No. 200 SWCC Poisson’s ratio Smoothness (IRI) MR Thickness MR Percent passing No. 200 SWCC R ig id P av em en t Transverse Cracking (JPCP) Thickness MR Erodibility index Loss of friction Groundwater depth MR Faulting (JPCP) MR Erodibility index Thickness Load transfer efficiency (LTE) MR Punchouts (CRCP) MR Base slab friction Thickness MR Groundwater depth Crack Width (CRCP) Base slab friction MR Thickness LTE MR Groundwater depth Smoothness (IRI) (JPCP) MR Erodibility index Base slab friction Thickness MR Smoothness (IRI) (CRCP) MR Base slab friction Thickness MR

5 However, recent investigations indicate that the performance predicted by Pavement ME Design generally shows low or no sensitivity to these underlying layers. A recent study conducted in the NCHRP Project 01-47 (2) reveals the following major problems:  Total rutting in flexible pavements is marginally sensitive to MR and SWCC of unbound layers and subgrade, non-sensitive to thickness of unbound layers.  Load-related cracking in flexible pavements is non-sensitive to the SWCC of unbound layers, marginally sensitive to SWCC of subgrade.  Faulting in JPCP is marginally sensitive to MR and erodibility, non-sensitive to the thickness of unbound layers.  Transverse cracking in JPCP is marginally sensitive to MR, thickness, and erodibility of unbound layers. To find the reasons for these problems, a better understanding of how the properties/thickness of unbound layers and subgrade affect pavement performance is needed, and this is detailed next. INFLUENCE OF UNBOUND LAYERS AND SUBGRADE ON PERFORMANCE OF FLEXIBLE AND RIGID PAVEMENTS Table 1 presents the inputs of unbound layers and subgrade required in Pavement ME Design for predicting the performance of flexible and rigid pavements. However, besides these parameters, recent studies have identified the pavement performance to be significantly affected by other characteristics of the underlying layers. According to a comprehensive literature review, researchers divided the factors into the following categories:  Material properties (e.g., modulus, shear strength).  Material behaviors responding to traffic and environmental (temperature and moisture) conditions (e.g., permanent deformation and erosion).  Structural characteristics (e.g., thickness of unbound layers). Table 2 to Table 5 summarize how each performance indicator is influenced by the factors of unbound layers and subgrade. The relevant literature is also given in these tables. Elaborated explanations for Table 2 to Table 5 are presented in Appendix A, “Annotated Bibliography of Influence of Unbound Layers and Subgrade.”

6 Table 2. Influential Factors of Unbound Layers on Performance of Flexible Pavements. Pe rf or m an ce   In di ca to rs   Material Properties  Material  Behaviors  Thickness Modulus  Shear Strength  Permanent Deformation Magnitude  Cross‐Anisotropy  Moisture  Sensitivity  To ta l R ut tin g  Total rutting  decreases as  modulus  increases (3, 4)  The amount of  permanent  deformation  significantly  increases  when  anisotropic  properties are  used (5)  Modulus has  a high  sensitivity in  change of  matric  suction that  represents  moisture  susceptibility;  high degree  of moisture  causes  decrease of  the modulus  (15–19)  Shear strength  directly affects  total rutting; it  decreases as  shear strength  increases (6–11)  Total rutting  increases as  permanent  deformation  of unbound  base course  increases  (12)  Rutting  decreases  with increase  of the  thickness of  the base layer  (4)  Lo ad ‐r el at ed  C ra ck in g  (A lli ga to r a nd   Lo ng itu di na l)  Load‐related  cracking would  easily occur  with reduced  modulus (4, 12)  Use of cross‐ anisotropy of  unbound base  course results  in less  estimated  fatigue  cracking life  (13)  A larger shear  strength  improves the  integrality of  supporting layers  and also  resistance to  load‐related  cracking (1)   N/A  The resistance  to load‐ related  cracking  would be  enlarged with  thick unbound  layers (3, 4)  Th er m al  C ra ck in g  Thermal  cracking is  accelerated by  loss of modulus  (14)  N/A  N/A  N/A  The greater  thickness of  the base layer  possibly helps  alleviate the  severity of  thermal  cracking (20)  Sm oo th ne ss  (I RI )  IRI decreases  with the  increase of  base modulus  (4)  Cross‐ anisotropy  affects total  rutting and  cracking,  which leads to  the change of  IRI (4)  High shear  strength results in  low IRI values (1,  11)  Permanent  deformation  of unbound  base is a  major  distress  resulting in  increase of  surface  roughness  (9)  Change of IRI  diminishes  with increase  of thickness of  the base layer  (4)

7 Table 3. Influential Factors of Unbound Layers on Performance of Rigid Pavements. Pe rf or m an ce   In di ca to rs   Material Properties  Material Behaviors  Thickness  Modulus  Shear  Strength  Erosion  Permanent  Deformation Magnitude  Cross‐Anisotropy  Moisture  Sensitivity  Tr an sv er se  C ra ck in g  (JP CP )  Transverse  cracking  would be  promoted  with low  modulus of  unbound  layers (3, 21)  Cross‐ anisotropy  greatly  affects  stress/  strain and  cracking (5,  13)   Modulus has  a high  sensitivity in  change of  matric suction  that  represents  moisture  susceptibility;  high degree of  moisture  causes  decrease of  the modulus  (14, 19, 31)  High shear  strength  prevents  occurrence of  transverse  cracking (22,  23)   N/A  N/A  Thickness  of baser  layer  directly  affects  amount of  transverse  cracking  (3)  Fa ul tin g  (JP CP )  Loss of  modulus of  unbound  base course  lead to  development  of faulting  (24)  N/A  Increase of  shear  strength  inhibits the  development  of faulting  (24)  Development  of erosion  accelerates  faulting (24– 26)  Greater  permanent  deformation  of unbound  base leads to  higher  potential of  faulting (27)  Faulting  decreases  with high  base  thickness  (3)  Pu nc ho ut s ( CR CP )  Reduction of  modulus of  unbound  base course  causes  punchouts  (28–30)  N/A  Potential for  punchouts is  greater when  shear  strength  decreases  (25, 28)  Erosion  intensifies  punchout (25,  28, 32)  N/A  Increase of  thickness is  an  effective  method to  control  punchouts  (3)  LT E  (JP CP  a nd   CR CP )  A higher  modulus of  unbound  base layer  improves LTE  (25, 33)   N/A  Unbound  layers with  high shear  strength have  good LTE (24)  Development  of erosion  causes low  LTE (25,  34)  N/A  Increase of  thickness  helps  improve  LTE (25)  Sm oo th ne ss  (I RI )  (JP CP  a nd  C RC P)   IRI decreases  with increase  in modulus of  base layer (3)  Cross‐ anisotropy  affects  cracking  and so IRI  (5, 13)  Increase of  shear  strength of  base layer  diminishes  roughness  (35, 36)  Erosion  aggravates IRI  (25,  34, 37)  Permanent  deformation  of unbound  base  increases  roughness  (9)  IRI  decreases  with  increase in  base layer  thickness  (3)

8 Table 4. Influential Factors of Subgrade on Performance of Flexible Pavements. Pe rf or m an ce   In di ca to rs   Material Properties  Material Behaviors  Modulus  Shear  Strength  Permanent  Deformation Magnitude  Cross‐Anisotropy  Moisture Sensitivity  To ta l R ut tin g  Total rutting  decreases as  modulus  increases (18)  Use of nonlinear  anisotropic model  of subgrade  affects  stress/strain  distribution, and  then influences  the inputs in  distress prediction  models (5, 38, 39)  A higher soil  suction  generates a  larger modulus  of subgrade  (40–45)  Total rutting  decreases as  shear  strength of  subgrade  increases  (46)  Total rutting  increases as  permanent  deformation of  subgrade  augments (46)  Lo ad ‐r el at ed   Cr ac ki ng   (A lli ga to r a nd   Lo ng itu di na l)  Resistance to  load‐related  cracking would  be enhanced  with increase of  modulus of  subgrade (2, 3,  18)  N/A  Lower permanent  deformation of  subgrade reduces  the probability of  load‐related  cracking (39)  Th er m al  C ra ck in g  N/A  N/A  N/A  N/A  Thermal cracking  is related to  shrinkage of  supporting  subgrade soils;  high permanent  deformation  would reduce the  resistance to  thermal cracking  (14)  Sm oo th ne ss  (I RI )  IRI has a  negative relation  with modulus of  subgrade (4, 47)  Use of nonlinear  anisotropic model  of subgrade  affects  stress/strain  distribution, and  then influences  the inputs in  distress prediction  models (5, 38, 39)  Soil suction is a  major factor for  prediction of  subgrade  modulus (41)  Decrease of  shear  strength of  subgrade  results in loss  of  smoothness  (1)  High permanent  deformation  exacerbates the  roughness of  pavement (9, 14) 

9 Table 5. Influential Factors of Subgrade on Performance of Rigid Pavements. Pe rf or m an ce   In di ca to rs   Material Properties  Material Behaviors  Modulus  Shear  Strength  Permanent  Deformation Magnitude  Cross‐Anisotropy  Moisture Sensitivity  Tr an sv er se  C ra ck in g  (JP CP )  Increasing modulus of  subgrade would  reduce transverse  cracking (3, 21)  Cross‐anisotropy  affects stress/strain  and then influences  the inputs in  distress prediction  models (5, 13)  Soil suction is a  major factor  for the  prediction of  modulus of  subgrade  materials; a  higher soil  suction  generates a  larger modulus  of subgrade  (41)  Increase of  shear  strength of  subgrade  raises the  resistance of  transverse  cracking (1)  High  permanent  deformation  leads to loss of  supporting  layers, which  could cause  development  of transverse  cracking (1)  Fa ul tin g  (JP CP )  Increase in modulus of  subgrade causes a  decrease in faulting  (48)  Higher shear  strength of  subgrade  layer helps  improve  resistance to  faulting (27)  High  permanent  deformation of  subgrade  increases the  possibility of  faulting (1, 49)  Pu nc ho ut s ( CR CP )  Punchout increases  with low k‐value of  subgrade (28, 29)  N/A  Punchout is  accelerated  with lower  shear  strength of  subgrade  (28)  Increase of  permanent  deformation of  subgrade  makes poorer  LTE; thus leads  to  development  of punchouts  (49)  LT E  (JP CP  a nd   CR CP )  LTE is increased by  high modulus of  subgrade (25)  N/A  Increase of  shear  strength  improves LTE  (25)  Loss of LTE  occurs with  high  permanent  deformation of  subgrade (28)  Sm oo th ne ss  (I RI )  (JP CP  a nd  C RC P)   IRI value diminishes  with the increase in  subgrade modulus (3)  Cross‐anisotropy  affects  cracking/faulting  and so IRI (5, 13)  Improvement  of shear  strength of  subgrade  layer could  increase  smoothness  (36)  Rutting  generated from  permanent  deformation of  subgrade is  associated with  increased  roughness (9) 

10 UNBOUND LAYER AND SUBGRADE MODELS FOR PERFORMANCE INFLUENCE Table 2 to Table 5 demonstrate various characteristics of unbound layers and subgrade that affect the performance of flexible and rigid pavements. Based on these results, researchers searched and identified the ME models that address such influence. More details of each model and associated parameter definitions are given in Appendix B, “Definitions of Model Parameters of Unbound Layer and Subgrade Models.” More specifically, the relevant models contain the following, which are summarized in Table 6 to Table 11:  Unbound layer models: o Current Pavement ME Design models for the base course of both flexible and rigid pavements. o Modulus models (Table 6), particularly those that incorporate the effects of the level of moisture in addition to the traffic-related stresses. The anisotropy of the base course is reflected in a separate model for the vertical modulus and the horizontal modulus. o Permanent deformation models (Table 7), which are sensitive to the changes of properties and thickness of the base course, and particularly the ones that predict a larger range of deformation that is close to the behavior of real unbound layer materials. o Shear strength models (Table 8), especially those models that include the effects of moisture and traffic-related stresses on the shear strength of the base course. o Erosion and faulting models (Table 9). o Thickness sensitive models (Table 11). This category refers to the models that have an influence on the thickness of the base course, which further affects the performance of the pavement. The moisture-sensitive, stress-dependent, and cross- anisotropic modulus models; moisture-sensitive shear strength models; stress- dependent ME permanent deformation models; and ME erosion models could contribute to this category.  Subgrade models: o Current Pavement ME Design models for the subgrade of both rigid and flexible pavements. o Modulus models (Table 6), particularly those that incorporate the effects of the level of moisture in addition to the traffic-related stresses on the stiffness of the subgrade. o Permanent deformation models (Table 7), especially those that predict a larger range of deformation that is close to the behavior of real soils. o Shear strength models (Table 8), particularly the ones that include the effects of moisture and traffic-related stresses on the shear strength of the subgrade. This property becomes important when attempting to reflect the performance of a pavement under heavy load, on a moisture-susceptible soil, or where there is poor drainage or there is slippage between the base course and subgrade on which it rests. o Foundation models (Table 10) that significantly reduce errors and variations of critical rigid pavement responses.

11 Table 6. Modulus Models of Unbound Layers and Subgrade. Model Type Model Formulation (detailed definitions of parameters in Appendix B) Material Type Literatures Nonlinear Stress- dependent Model 2 1 3 k RM k  Granular Base (50) Nonlinear Stress- dependent Model 2 1 k RM k  Granular Base (51) Nonlinear Stress- dependent Model  2 3 1R dM k k k    1 dk   2 4 1R dM k k k   1 dk  Subgrade Soil (52) Nonlinear Stress- dependent Model ' ' d R d a bM     Subgrade Soil (53) Nonlinear Stress- dependent Model 32 1 kk R dM k   Granular Base/ Subgrade Soil (54) Nonlinear Stress- dependent Model 2 3 1 1 k k oct R IM k Pa Pa Pa             Granular Base/ Subgrade Soil (55) Nonlinear Stress- dependent Model 2 3 1 1 1 k k oct R IM k Pa Pa Pa             Granular Base/ Subgrade Soil (15) Nonlinear Stress- dependent Model 2 1 2 R I JM M Pa R Pa Pa             Granular Base (56) Moisture-sensitive Model   log 1 exp ln R Ropt m opt M b aa bM k S S a          Granular Base/ Subgrade Soil (57) Moisture-sensitive and Stress- dependent Model    2 3 1R d s a wM k k k k u u        2 4 1R d s a wM k k k k u u     Subgrade Soil (58) Moisture-sensitive and Stress- dependent Model 2 3 1 4 1 3 k koct R I kM k Pa Pa Pa             Granular Base/ Subgrade Soil (59) Moisture-sensitive and Stress- dependent Model 2 3 1 1 3 k km oct R I fhM k Pa Pa Pa              Granular Base/ Subgrade Soil (60) Moisture-sensitive and Stress- dependent Model 2 3 1 1 1 3 3 k km oct oct R II f h M k Pa Pa Pa                        Granular Base/ Subgrade Soil (14) Moisture-sensitive and Stress- dependent Model   21 k R d w mM k     Subgrade Soil (41)

12 Moisture-sensitive and Stress- dependent Model 2 3 1 1 k k w m oct R a a a M k P P P                 Subgrade Soil (61) Moisture-sensitive and Stress- dependent Model ' 2 4 4 ' 1 3 1 1o k k k m mnet w sat oct R a a a a uM k P P P P                         Granular Base (19) Moisture-sensitive and Stress- dependent Model   2 3 6 1 7 3 k k b oct R a us a a w a a kM k p k k p p p                     Subgrade Soil (18) Moisture-sensitive and Stress- dependent Model 2 3 4 1 3 1 k k w oct R a a a k SVM k P P P               Granular Base/ Subgrade Soil (62) Stress-dependent and Cross- anisotropic Model 2 3 1 1 1 ; k k V oct R a a a H R VH V V R R IM k P P P M Gn m M M                Granular Base (63) Stress-dependent and Cross- anisotropic Model 2 3 5 6 8 9 1 4 7 ; k k k k V Hoct oct R a R a a a a a k k oct VH a a a M k P M k P P P P P G k P P P                                           Granular Base (64) Moisture- sensitive, Stress- dependent, and Cross-anisotropic Model 2 3 1 1 3 ; k k V m oct R a a a H R VH V V R R I fhM k P P P M Gs r M M                 Granular Base (31) Regression Models for Stress- dependent Model Coefficients     1 2 3 1.3577 0.0106 % 0.0437 0.5193 0.0073 4 0.0095 40 0.0027 200 0.003 0.0049 1.4258 0.0288 4 0.0303 40 0.0521 200 0.0251 % 0.0535 0.0672 0.0026 0.0025 0.6055 k clay wc k P P P LL wopt k P P P silt LL wopt opt wcs wopt                            Subgrade Soil (65) Regression Models for Moisture-sensitive and Stress- dependent Model Coefficients               1 2 3 ln 137.19 13.60ln 4.35ln 0.62ln 36.14 0.04 3.81ln 0.22 0.77ln 4.39 0.45ln 0.01 0.05 0.15ln d A T A s T d s T k k ofc a k pfc a                      Granular Base (31)

13 Empirical Regression Model for MR  0.642555rM CBR Granular Base/ Subgrade Soil (66) Empirical Regression Model for MR 1155 555rM R  Granular Base/ Subgrade Soil (66) Empirical Regression Model for MR 30000 0.14 i r aM       Granular Base/ Subgrade Soil (67) Empirical Regression Model for MR   0.64 752555 1 0.728r M wPI        Granular Base/ Subgrade Soil (67) Empirical Regression Model for MR 0.64 1.12 2922555rM DCP       Granular Base/ Subgrade Soil (66) Table 7. Permanent Deformation Models of Unbound Layers and Subgrade. Model Type Model Formulation (detailed definitions of parameters in Appendix B) Material Type Literatures Non-stress- dependent ME Model  1 P r N N N         Granular Base/ Subgrade Soil (68) Non-stress- dependent ME Model p b r aN    Granular Base/ Subgrade Soil (69) Non-stress- dependent ME Model 0 N p e         Granular Base/ Subgrade Soil (70) Non-stress- dependent ME Model    , 0 kzp p zz e   Subgrade Soil (71) Stress- dependent ME Model 0 N p s v r e               Granular Base/ Subgrade Soil (67) Stress- dependent ME Model 6 0 1 2 6 0 1 2 log .log p oct r oct ka a a Pa Pa kb b b N Pa Pa                                       Granular Base (69) Stress- dependent ME Model 1 b p RCN R    ' f qb d c q         Granular Base (72)

14 Stress- dependent ME Model max D fB C p dAN            Granular Base (11) Stress- dependent ME Model    0 2 1 m nN p e J I K           Granular Base (73) Regression Models for Pavement ME Design Model Coefficients 60 6 4 2 3 2 6 log 0.80978 0.06626 0.003077 10 log 0.9190 0.03105 0.001806 1.5 10 log 1.78667 1.45062 3.784 10 2.074 10 1.05 10 c r r c r c c r W E W E W W E                                          Granular Base (70) Regression Models for Pavement ME Design Model Coefficients     910 0 1 9 9 0.15 20 2 log 0.61119 0.017638 4.8928510 1 10 r c e e W                                          Granular Base (66) Regression Models for Pavement ME Design Model Coefficients 0ln 10.24 0.03 0.10 0.88 3.95 ln ln 6.74 0.02 0.04 0.85 0.03 0.13 ln 10.17 2.75 ln 0.05 2.00 1.61ln 0.34 A T G G T d G A T MBV pfc a MBV pfc a a pfc a a                         Granular Base (74)

15 Table 8. Shear Strength Models of Unbound Layers and Subgrade. Model Type Model Formulation (detailed definitions of parameters in Appendix B) Material Type Literatures Non-moisture- sensitive Model tannc    Granular Base/ Subgrade Soil (75) Moisture- sensitive Model    ' tan ' tan b f n a a wc            Subgrade Soil (76) Moisture- sensitive Model   tantan n b a wc u u     Subgrade Soil (77) Moisture- sensitive Model    ' tan ' tan b f n a a wc S           Subgrade Soil (78) Moisture- sensitive Model    ' tan ' tan b f n a a wc            Subgrade Soil (79, 80) Moisture- sensitive Model         ' tan ' tanf n a a w a w a w b c                         Subgrade Soil (81) Moisture- sensitive Model  ' tan ' ' 1676.624 2.088 13.260 0.113 270.722ln 38.778 ' 2.827 0.016 0.0005 0.051 0.763ln 0.008 n m A A d G A S d c c MBV a fh a MBV a pfc                          Granular Base (74) Moisture- sensitive Model 2 tan 83.95 1.58 40 2.57 0.043 40 6.88 0.14 0.81 tan 1.61 0.96 0.88 4.13 31.82 n N N sN sb c c N n N PL G PI n G                       Granular Base/ Subgrade Soil (82)

16 Table 9. Erosion Models of Unbound Layers. Model Type Model Formulation (detailed definitions of parameters in Appendix B) Material Type Literatures Empirical Model ESALg         Granular Base (83) Empirical Model log 1.07 0.34 i dP m ESAL f m D       Granular Base (84) Empirical Model exp 2.884 1.652 log 10,000 ESAL DE NPI               Granular Base (85) Empirical Model 36.67 2.884 1.652 log 10, 000 P N PI E SAL D E N PI F                  Granular Base (86) Empirical Model  0.1031 2 1 log 14.524 6.777 9.0 100 m i i i N C P C nPercent erosion damage N      Granular Base (87) Empirical Model Table-based Erodibility Class Assessment Granular Base (66) ME Model    0% D N vf Erosion f e        Granular Base/ Subgrade Soil (24) Table 10. Foundation Models of Subgrade. Model Type Model Formulation (detailed definitions of parameters in Appendix B) Material Type Literatures No-shear Model (x,y) kw(x,y)p  Granular Subbase/ Subgrade Soil (88) No-shear Model 2(x,y) kw(x,y) (x,y)p T w   Granular Subbase/ Subgrade Soil (89) No-shear Model 2 2(x,y) kw(x,y) (x,y)p D w   Granular Subbase/ Subgrade Soil (90) Shear-included Model 2(x,y) kw(x,y) G (x,y)p w   Granular Subbase/ Subgrade Soil (91) Shear-included Model 2 2(1 )p kw Gk G p w c c       Granular Subbase/ Subgrade Soil (92)

17 Table 11. Thickness Sensitive Models of Unbound Layers. Model Category Model Type Model Formulation Literatures Modulus Models Nonlinear Stress-dependent Models in Table 6 See Table 6 See Table 6 Moisture-sensitive and Stress-dependent Models in Table 6 Stress-dependent and Cross-anisotropic Models in Table 6 Moisture-sensitive, Stress-dependent, and Cross- anisotropic Model in Table 6 Permanent Deformation Models Stress-dependent ME Models in Table 7 See Table 7 See Table 7 Regression Models for Pavement ME Design Model Coefficients in Table 7 Shear Strength Models Moisture-sensitive Models in Table 8 See Table 8 See Table 8 Erosion Models ME Models in Table 9 See Table 9 See Table 9