NCHRP Calibration of Fracture Predictions to Observed Reflection Cracking in HMA Overlays

Transcription

1 NCHRP 1-41 Calibration of Fracture Predictions to Observed Reflection Cracking in HMA Overlays Robert L. Lytton Fang-Ling Tsai, Sang-Ick Lee Rong Luo, Sheng Hu, Fujie Zhou Pavement Performance Prediction Symposium July 16, 2009, Laramie, Wyoming

2 MEPDG vs. NCHRP 1-41 MEPDG NCHRP 1-41 Traffic Material Properites Interlayer INPUT Climate-EICM Pavement Structure Existing Pavement Conditions Pavement Response (σ, ε) Model: Multi-layer elastic system MODELS Pavement Response Model Stress Intensity Factor (SIF) Artificial Neural Network (ANN) Pavement Distress Models Pavement Distress Model Reflection Cracking (Thermal, Shearing, Bending) Pavement Performance Predictions OUTPUT Pavement Performance Prediction: Reflection Cracking Extent and Severity 2

3 Reflection Cracking Modeling Traffic input New temperature model (Model source: Dr. C. J. Glover, Department of Chemical Engineering, Texas A&M Univ.) Load transfer efficiency Fracture property of asphalt mixtures (A and n) Artificial neural network (ANN) models Stress intensity factor (SIF) model Mixture modulus Reflection cracking models Model calibration 3

4 Mechanisms of Reflection Cracking Traffic loading Bending Shearing Daily temperature change Thermal stress 4

5 Field Data for Calibration and Validation of Reflection Cracking Models Long-Term Pavement Performance (LTPP) database Reflection cracking projects for Texas Department of Transportation (TxDOT) Reflection cracking study at Applied Research Associates (ARA), New York City 5

6 Expected Model Fast Based on current MEPDG parameter Accounts for three cracking mechanisms Easy to calibrate locally Easy to use in design 6

7 Reflection Cracking in HMA Overlay Thermal Stress Bending Load Stress Shearing Load Stress Temperature (ΔT) Overlay H overlay, E overlay,α overlay C Existing Asphalt Layer H Existing, E Existing,α Existing Base Course Subbase Subgrade 7

8 Flow Chart of NCHRP 1-41 Selected Test Sections 10 models Selected Test Sections Field Data Collection Distress Traffic Pavement Weather Field Data Analysis Distress Traffic Axle load distribution Field Data Collection Temperature Model Weather data Modeling temperature with depth New temperature model Pavement Data Calibration Process Asphalt modulus Field Data Analysis Falling weight deflectometer Artificial neural network Calculated number of days Thermal (2) Shear (2) Bending (1) Calibration to field distress Five calibration coefficients Three levels of damage Temperature Model Crack Propagation Model Calibration Process Final Model of Reflection Cracking Pavement Data Analysis Crack Propagation Model Temperature Traffic shear Traffic bending Artificial neural network models Modulus Stress intensity factors Viscoelastic thermal stress Crack growth Layers Material properties Non-destructive testing 8

9 LTPP Test Sections (1/2) Category Description Climate Zone WF DF WNF DNF Total AC/AC AC, then AC OL AC/Mill/AC AC, then Mill+AC OL AC/SC/AC AC/FC/AC AC w/ Interlayer, then AC OL CRC/AC CRC, then AC OL JPC/AC JRC/AC PCC, then AC OL Total

10 LTPP Test Sections (2/2) WF DF WNF DNF 10

11 NYC Sections with Interlayer Twelve test sections for each joint spacing 15 ft 20 ft Measured cracking ratio Various reinforcing interlayers 11

12 Texas Sections with Interlayer Investigation of reflection cracking control treatment Selection of 3 test locations in Texas Evaluation of tested pavement in each spring Pavement Overlay District Type Date Amarillo AC/Interlayer/AC Jun Dry-Cold Amarillo Wet-Cold Waco Pharr AC/Interlayer/AC Apr Waco AC/Interlayer/JPC Oct Dry-Warm Pharr Wet-Warm 12

13 Flow Chart of NCHRP 1-41 Field Data Analysis Distress Traffic Axle load distribution Selected Test Sections Field Data Collection Field Data Collection Distress Traffic Pavement Weather Field Data Analysis Temperature Model Pavement Data Analysis Crack Propagation Model Calibration Process Final Model 13

14 Data Collected from LTPP Sections Section (WF zone in New Jersey) AC/AC overlay rehabilitation: July 27 th, 1992 Transverse crack length before overlay: 88.2m Reflective Crack Length (m) /27/92 12/9/93 4/23/95 9/4/96 1/17/98 6/1/99 10/13/00 Observation Date 2/25/02 7/10/03 11/21/04 Crack Length (ratio) No. of Days after Overlay Observed crack length Ratio of reflection crack length to max. length 14

15 Data Collected from NYC Sections Portland Cement Concrete (PCC) with 20 ft joint spacing Interlayer material: composite PCC joint crack length: 240 ft (10 joints 24 ft) Reflective Crack Length (ft.) /1/00 12/14/01 4/28/03 9/9/04 Observation Date 1/22/06 6/6/07 10/18/08 Crack Length (ratio) No. of Days after Overlay Observed crack length Ratio of reflection crack length to max. length 15

16 Data Collected from Texas Sections Amarillo District Interlayer material: composite Transverse crack length before overlay: 46 m Reflective crack Length (m) /1/02 6/1/03 5/31/04 5/31/05 Observation Date 5/31/06 5/31/07 5/30/08 Reflective Crack Length (ratio) No. of Days after Overlay Observed crack length Ratio of reflection crack length to max. length 16

17 Development of Reflection Cracking The amount and severity of reflection cracking follows a sigmoidal curve (S-shape) Percent of Original AREA (%) Crack Length High+Medium+Low Severity High+Medium Severity High Severity Traffic or Time 17

18 Modified Reflection Cracking Model Length of transverse crack after overlay Observed by time where D(N i ) = N i i Ni DN ( ) = e i ρ = number of days after overlay β Measured crack length at N i Crack length on original surface before overlay = i th crack observation ρ and β = calibration coefficients 18

19 Calibration of Reflection Cracking Model Given severity levels ρ H and β H for high severity level ρ M and β M for medium/high severity levels ρ L and β L for low/medium/high severity levels For specific types of pavement HMA overlay on HMA surface HMA overlay on PCC surface, etc 19

20 Calibrated Models for LTPP Sections LTPP section (WF zone in New Jersey) AC/AC overlay Reflective Crack Length (ratio) measured predicted H+M+L H+M H No. of Days after Overlays Calibrated Parameters Values Severity Parameter β ρ H+M+L H+M H N/A N/A 20

21 Calibration Model for NYC Sections NYC test section with 20 ft joint spacing AC/PCC overlay with composite interlayer Reflective Crack Length (ratio) measured predicted H+M+L H+M H No. of Days after Overlay Calibrated Parameters Values Severity Parameter β ρ H+M+L H+M H N/A N/A 21

22 Calibration Model for Texas Sections Texas test sections (Amarillo District) AC/AC overlay with composite interlayer Reflective Crack Length (ratio) measured predicted H+M+L H+M H No. of Days after Overlay Calibrated Parameters Values Severity Parameter β ρ H+M+L H+M N/A N/A H N/A N/A 22

23 Traffic Data Overview of traffic data for AC Overlay Design Axle Load Distribution Classification of Traffic Load Modeling Cumulative Axle Load Distribution (CALD) Determination of CALD of LTPP Test Sections CALD curve Calculated parameters Result of LTPP section (example of ) 23

24 Axle Load Distribution Factor (1/2) Percentage of total axles in each load interval for a specific axle type and vehicle class Axle type and load interval Axle Type Single Tandem Tridem & Quad Axle Load Intervals 3,000 ~ 40,000 lb. at 1,000 lb. intervals 6,000 ~ 80,000 lb. at 2,000 lb. intervals 12,000 ~ 102,000 lb. at 3,000 lb. intervals Vehicle class: Class 4 to 13 Tires: single and dual 24

25 Axle Load Distribution Factor (2/2) Weigh-In-Motion (WIM) data Distribution of axle loads in a vehicle class Number of axles measured in each load interval = Total number of axles in all load intervals Normalized Axle Load Distribution Factor (ALDF) = 100 for each axle type of each vehicle class 25

26 Categorization of Traffic Load Eight categories on axle type and number of tires Vehicle Class Single Axle Tandem Axle Tridem Axle Quad Axle No. 1 Single Tire No. 3 No. 5 No. 7 Dual Tires No. 2 No. 4 No. 6 No. 8 26

27 Model Tire Load Uniform Pressure, p Width, W Tire Length, L Model Tire Load: P = p LW

28 Cumulative Axle Load Distribution Cumulative Load Distribution P 3 = 1 P 2 Maximum load Upper load limit* P i = f (L i ) P 1 Lower load limit *Upper load limit: Federal Regulation for maximum allowable axle weight L 1 L 2 L 3 Tire Length Legal axle load Illegal axle load 28

29 Models for Cumulative Axle Load Distribution Gompertz model y = αexp exp( β γx) - α is the upper asymptote - β indicates the width - γ indicates the slope Y e -1 α (=1.0 for CALD curve) γ γ β 1 β 2 X Modified model for Cumulative Axle Load Distribution (CALD) PL = β γl ( i) exp exp( i) 29

30 Characteristics of Axle Load Distribution Axle Type Tires Tire width (in.) Tire Pressure (PSI) Axle Load (lb.) Minimum (L 1 ) Max. Allowable* (L 2 ) Single Tandem Tridem Quad Single Dual (< 6000 lb) 120 (> 6000 lb) Single Dual Single Dual Single Dual ,000 20,000 6,000 34,000 12,000 42,000 12,000 42,000 *Federal regulation for maximum allowable axle weight 30

31 CALD for LTPP Test Section (Single Axle) LTPP test section (Indiana) Single axle/single tire (2004) Cumulative Axle Load Distribution P 3 P 2 P 1 0 L L 2 15 L Tire Length (in.) Measured Predicted Cumulative Axle Load Distribution Curve CALD on tire length Tire Length (in.) CALD L P L P L P Model parameter Parameter Value β γ R

32 CALD for LTPP Test Section (Quad Axle) Cumulative Axle Load Distribution LTPP test section (Indiana) Quad axle/dual tires(2004) P 3 P 2 P 1 0 L 1 2 L 2 4 L Tire Length (in.) Measured Predicted Cumulative Axle Load Distribution Curve CALD on tire length Tire Length (in.) CALD L P L P L P Model parameter Parameter Value β γ R

33 All CALD on Tire Length LTPP test section (Indiana) CALD CALD CALD CALD Tire Length (in.) Tire Length (in.) Tire Length (in.) Tire Length (in.) Single Axle/Single Tire Single Axle/Dual Tires Tandem Axle/Single Tire Tandem Axle/Dual Tires CALD CALD CALD CALD Tire Length (in.) Tire Length (in.) Tire Length (in.) Tire Length (in.) Tridem Axle/Single Tire Tridem Axle/Dual Tires Quad Axle/Single Tire Quad Axle/Dual Tires 33

34 Calculated Model Parameters Axle Single Tandem Tire Model Parameter Tire Length (in.) CALD* β γ R 2 L 1 L 2 L 3 P 1 P 2 P 3 Single Dual Single Dual Tridem Single Dual Quad Single Dual *Cumulative Axle Load Distribution 34

35 Flow Chart of NCHRP 1-41 Selected Test Sections Field Data Analysis Field Data Collection Temperature Model Pavement Data Analysis Pavement Data Layers Material properties Non-destructive testing Crack Propagation Model Calibration Process Final Model 35

36 Pavement Data Analysis Pavement layer structure Falling Weight Deflectometer (FWD) data (including temperature) Backcalculation of layer moduli (using MODULUS) 36

37 Relaxation Modulus by FWD E ( t) log E E E and T are measured from FWD 1 ( t, T ) = ( t) = = log E n i= 1 E E e i ( t, T ) + E t T i 1 + m t a T mix m mix log t a T Viscoelastic Thermal Stress 37

38 Flow Chart of NCHRP 1-41 Selected Test Sections Field Data Collection Field Data Analysis Temperature Model Pavement Data Analysis Temperature Model Weather data Modeling temperature with depth New temperature model Crack Propagation Model Calibration Process Final Model 38

39 Reflection Cracking in HMA Overlay Thermal Stress Bending Load Stress Shearing Load Stress Temperature (ΔT) Overlay H overlay, E overlay,α overlay C Existing Asphalt Layer H Existing, E Existing,α Existing Base Course Subbase Subgrade 39

40 Weather Data Hourly Solar Radiation Daily Wind Speed Hourly Wind Speed Daily Air Temperature Hourly Air Temperature Emissivity Coefficient Absorption Coefficient Albedo a d Flow Chart of Predicting Thermal Crack Growth No Pavement Temperature (ΔT) Viscoelastic Thermal Stress σ(t) Gradation Volumetric Composition Frequency (f c) Viscosity (η) Binder Properties 1999 Model 1999 Model? 2006 Model? 2006 Model Relaxation Modulus at Crack Tip (Artificial Neural Network Model) Gradation Volumetric Composition Phase Angle (δ b) Shear Modulus of Asphalt (G*) Thermal Stress Intensity Factor (SIF) (Artificial Neural Network Model) Fracture Properties A,n Crack Growth ΔC=A[J] n ΔN Is ΣΔC Overlay Thickness Yes No. of Days N ft1,n ft2 40

41 Pavement Temperature Model Enhanced Integrated Climate Model (EICM) Model developed by Dr. Charles Glover s research group, Department of Chemical Engineering, Texas A&M. α Downwelling longwave radiation εa Outgoing longwave radiation ε Heat convection by wind Ta α : Albedo T a : Air temperature q s : Solar radiation ε : emissivity coefficient ε a : absorption coefficient pavement Heat conduction к к: Thermal conductivity (Model source: Xin Jin, Rongbin Han, and Dr. Charles Glover, Department of Chemical Engineering, Texas A&M University) 41

42 Pavement Temperature Prediction EICM Model vs. Glover Model EICM surface EICM 14cm Field surface Field 14cm Glover model surface Glover model 14cm Field surface Field 14cm Temperature (C) Days Typical daily pavement temperature prediction using EICM model Ahmed et. al (Transportation Research Record 1913, 2005) Typical daily pavement temperature prediction using Glover model 42

43 Pavement Temperature Prediction Hourly Solar Radiation Hourly Air Temperature Hourly Wind Speed Emissivity Coefficient Absorption Coefficient Albedo Thermal conductivity coefficients a and d Pavement temperature at different depths 43

44 Climate Data Collection Hourly climate data ( ) Solar radiation (Wh/m 2 ) Daily climate data ( ) Air temperature ( C): mean, maximum & minimum Wind speed (m/s) 44

45 Modeled Hourly Air Temperature 25 Location of pavement section: Mills County, Texas. 20 Air Temperature ( ⁰C) Hours Hourly Air Temperature Max. Daily Air Temperature Min. Daily Air Temperature 45

46 Albedo Distribution in Winter

47 Albedo Distribution in Summer

48 Calculated Pavement Temperature Location of pavement section: Mills County, Texas. Pavement Temperature Changing by Depth Seal Coat Overlay Seal Coat Depth (m) Existing Layer Temperature ( C) 6:00 AM 12:00 PM 3:00 PM 8:00 PM Base Subbase Subgrade 48

49 Flow Chart of NCHRP 1-41 Selected Test Sections Field Data Collection Field Data Analysis Temperature Model Pavement Data Analysis Crack Propagation Model Calibration Process Final Model of Reflection Cracking Crack Propagation Model Temperature Traffic shear Traffic bending Artificial neural network models Modulus Stress intensity factors Viscoelastic thermal stress Crack growth 49

50 Weather Data Hourly Solar Radiation Daily Wind Speed Hourly Wind Speed Daily Air Temperature Hourly Air Temperature Emissivity Coefficient Absorption Coefficient Albedo a d Flow Chart of Predicting Thermal Crack Growth No Pavement Temperature (ΔT) Viscoelastic Thermal Stress σ(t) Gradation Volumetric Composition Frequency (f c) Viscosity (η) Binder Properties 1999 Model 1999 Model? 2006 Model? 2006 Model Relaxation Modulus at Crack Tip (Artificial Neural Network Model) Gradation Volumetric Composition Phase Angle (δ b) Shear Modulus of Asphalt (G*) Thermal Stress Intensity Factor (SIF) (Artificial Neural Network Model) Fracture Properties A,n Crack Growth ΔC=A[J] n ΔN Is ΣΔC Overlay Thickness Yes No. of Days N ft1,n ft2 50

51 Weather Data Hourly Solar Radiation Daily Wind Speed Hourly Wind Speed Daily Air Temperature Hourly Air Temperature Emissivity Coefficient Absorption Coefficient Albedo a d Flow Chart of Bending Crack Growth Daily Traffic Vehicle Class Distribution Pavement Temperature (ΔT) 1999 Model Gradation Volumetric Composition Frequency (f c) Viscosity (η) 1999 Model? 2006 Model? Binder Properties 2006 Model Gradation Volumetric Composition Phase Angle (δ b) Shear Modulus of Asphalt (G*) No Each Vehicle Class (Axle and Tire Loads) Layer Relaxation Modulus (Artificial Neural Network Model) Bending Stress Intensity Factor (SIF) (Artificial Neural Network Model) Fracture Properties A,n Crack Growth ΔC=A[J] n ΔN Is ΣΔC Overlay Thickness? Yes No. of Days N fb 51

52 Weather Data Hourly Solar Radiation Daily Wind Speed Hourly Wind Speed Daily Air Temperature Hourly Air Temperature Emissivity Coefficient Absorption Coefficient Albedo a d Flow Chart of Shearing Crack Growth Daily Traffic Vehicle Class Distribution Pavement Temperature (ΔT) 1999 Model Gradation Volumetric Composition Frequency (f c) Viscosity (η) 1999 Model? 2006 Model? 2006 Model Binder Properties Gradation Volumetric Composition Phase Angle (δ b) Shear Modulus of Asphalt (G*) No Each Vehicle Class (Axle and Tire Loads) Layer Relaxation Modulus (Artificial Neural Network Model) Shear Stress Intensity Factor (SIF) (Artificial Neural Network Model) Fracture Properties A,n Crack Growth ΔC=A[J] n ΔN Is ΣΔC Overlay Thickness? Yes No. of Days N fs1,n fs2 52

53 Modeling of Relaxation Modulus by Artificial Neural Network (ANN) (1999 Model) Witczak 1999 Model Input R 2 Output Gradation Volumetric Binder Data Witczak ANN 3/4 (%) 3/8 (%) #4 (%) #200 (%) V a (%) V beff (%) η 10 6 poise f c HZ E* psi Predicted IE*I (GPa) Observed IE*I (GPa) Witczak 1999 ANN

54 Modeling of Relaxation Modulus by Artificial Neural Network (ANN) (2006 Model) Witczak 2006 Model 3/4 (%) Input R Output 2 Gradation Volumetric Binder Data Witczak ANN 3/8 #4 #200 V a V beff Log G* δ c E* psi (%) (%) (%) (%) (%) 10 6 psi deg 60 Predicted IE*I (GPa) Observed IE*I (GPa) Witczak 2006 ANN

55 AC InterLayer LevelingCourse AC-H AC InterLayer LevelingCourse AC AC InterLayer LevelingCourse AC-M AC InterLayer LevelingCourse AC-L Thermal Cases AC Over AC and AC Mill AC AC Over PCC AC Over SC Over AC Pavement Structural Cases Pavement Model Pure Bending Load Cases Pure_Bending_ACoverAC_DualTire_Together Pure_Bending_ACoverAC_DualTire_Together (Only Positive) Pure_Bending_ACoverAC_SingleTire_Together Pure_Bending_ACoverAC_SingleTire_Together (Only Positive) Pure_Bending_ACPCC_SingleTire_Together Traffic Load Cases Pure_Bending_ACPCC_SingleTire_Together (Only Positive) AC_AC_Shearing_BendPart_dualTire AC_AC_Shearing_ShearPart_dualTire AC_PCC_Shearing_ShearPart_dualTire Shearing Load Cases AC_AC_Shearing_BendPart_singleTire AC_AC_Shearing_ShearPart_singleTire AC_PCC_Shearing_ShearPart_singleTire 55

56 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (1/9) AC_InterLayer_LevelingCourse_AC_slip_L AC_InterLayer_LevelingCourse_AC_slip_M 56

57 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (3/9) AC_AC AC_PCC 57

58 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (4/9) Pure_Bending_AC_AC_Dual_Tire_Together Pure_Bending_AC_AC_Dual_Tire_Together (Only Positive) 58

59 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (5/9) Pure_Bending_AC_AC_Single_Tire_Together Pure_Bending_AC_AC_Single_Tire_Together (Only Positive) 59

60 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (6/9) Pure_Bending_AC_PCC_Single_Tire_Together Pure_Bending_AC_PCC_Single_Tire_Together (Only Positive) 60

61 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (7/9) AC_AC_Shearing_ BendPart_Dual_Tire AC_AC_Shearing_ ShearPart_Dual_Tire 61

62 Modeling of Stress Intensity Factor (SIF) by Artificial Neural Network (ANN) (9/9) AC_PCC_Shearing_ ShearPart_Dual_Tire AC_PCC_Shearing_ ShearPart_Single_Tire 62

63 Fracture Properties of Asphalt Mixtures Paris Law Traffic c N K I Crack length Load repetitions [ 2 ] Healing Shift Factor ( ), K = Bending, shearing stress intensity factor II An, = Fracture coefficients a k = = dc dn = A K + K a = Viscoelastic stress pulse effect n I II k 63

64 Fracture Properties of Asphalt Mixtures SHRP Formulation (1/3) g1 n= g0 + mmix g g log A= g + log D + logσ mmix mmix Coefficient Climatic Zone Wet-Freeze Wet-No Freeze Dry-Freeze Dry-No Freeze g g g g g g g t 64

65 Fracture Properties of Asphalt Mixtures SHRP Formulation (2/3) Tensile Strength EtT (, ) 1000 r = in / mm, T = 77 F, σ t = > Temperature EtT (, ) 1000 r = 0.5 in / mm, T = 77 F, σ t = > Traffic

66 Fracture Properties of Asphalt Mixtures SHRP Formulation (3/3) Climate Zone Coefficient t o w rm R T d ( C) C 1 C 2 G g (GPa) Wet-Freeze Wet-No Freeze E Dry-Freeze Dry-No Freeze

67 Binder Properties: CAM Model 1.E+08 Shear Modulus of Binder (Pa) 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 Frequency (rad/sec) 1.E+07 1.E+06 1.E+05 1.E+04 1.E+03 1.E+02 1.E+01 1.E+00 Bryan Binder PP2+0Month (Measured) Bryan Binder PP2+0Month (Modeled) Bryan Binder PP2+3Month (Measured) Bryan Binder PP2+3Month (Modeled) Bryan Binder PP2+6Month (Measured) Bryan Binder PP2+6Month (Modeled) 67

68 Determination of m mix for Fracture Model log [ E ] mix Calculated with ANN- Witczak Model 1 m mix Loading time log a T t Emix = E1 a t m mix 68

69 Determination of Compliance Coefficient for Fracture Model D 1 = E 1 sin π ( m π ) m mix mix 69

70 Viscoelastic Stress Pulse Effect 4.0 ft 4.0 ft Overlay L j L j L j Old Surface Crack or Joint 4.0 ft 4.0 ft 5.0 ft L j L j L j 5.0 ft W ( t ) Load Wave Shape (18 + L j ) ft. [W (t)] n (0.72) n (0.92) n (0.92) n (0.84) n (0.84) n (0.84) n (0.76) n (0.76) n (0.72) n (0.095) n (0.095) n t 70

71 Viscoelastic Stress Pulse Effect 4.0 ft 5.0 ft L j 4.0 ft L j 5.0 ft Overlay L j Old Surface L j Crack or Joint W ( t ) Load Wave Shape L j L j (14 + L j ) ft. (1.11) n (1.11) n L j L j [W (t)] n (1.11) n (1.11) n L j L j t 71

72 Flow Chart of NCHRP 1-41 Selected Test Sections Field Data Collection Field Data Analysis Temperature Model Pavement Data Analysis Calibration Process Asphalt modulus Falling weight deflectometer Artificial neural network Calculated number of days Thermal (2) Shear (2) Bending (1) Calibration to field distress Five calibration coefficients Three levels of damage Crack Propagation Model Calibration Process Final Model of Reflection Cracking 72

73 Calculated Number of Days Position II N fs2 N ft2 Overlay Position I N fb1 N fs1 N ft1 C Bending Stress Shear Stress Thermal Stress N fb1 = Number of days for crack growth due to bending to reach Position I. N ft1 = Number of days for thermal crack growth to reach Position I. N fs1 = Number of days for crack growth due to shearing stress to reach Position I. N ft2 = Number of days for thermal crack growth to go from Position I to Position II. N fs2 = Number of days for crack growth due to shearing stress to go from Position I to Position II. 73

74 Calibration Quantities 100% Percent of Reflected 36.8% High + Medium+ Low Medium+ High High severity 0 ρ LMH ρ MH ρ H Number of Days Damage N ft1 N ft1 N ft 2 ρlmh = N ft1 α0 + α1 + α2 + N ft 2 α3 + α 4 N fs1 N fb1 N fs 2 Calibration Coefficients : α, α, α, α, α, β LMH 74

75 Another Set of Calibration Coefficients N ft1 N ft1 N ft 2 ρmh = N ft1 α5 + α6 + α7 + N ft 2 α8 + α 9 N fs1 N fb1 N fs 2 Calibration Coefficients : α, α, α, α, α, β MH N ft1 N ft1 N ft 2 ρh = N ft1 α10 + α11 + α12 + N ft 2 α13 + α 14 N fs1 N fb1 N fs 2 Calibration Coefficients : α10, α11, α12, α13, α14, β H 75

76 Predicted vs. Observed High Severity Damage Opened to Traffic in Winter ρ_h_predicted ρ_h_observed β_h_predicted β_h_observed High severity Regression results of ρ and β for AC over JPC/JR pavement and Wet-Freeze climate zone beginning service during the winter. 76

77 Predicted vs. Observed High Severity Damage Opened to Traffic in Summer ρ_h_predicted β_h_predicted ρ_h_observed β_h_observed High severity Regression results of ρ and β for AC over JPC/JRC pavement and Wet- Freeze climate zone beginning service during the summer. 77

78 Cracking Extent and Severity: AC over AC, WF % Total Length of Cracks L+M+H M+H H 100/e Number of Days AC over AC Pavement at Anchorage, Alaska (WF) 78

79 Cracking Extent and Severity: AC over JRC, WF % Total Length of Cracks L+M+H M+H H 100/e Number of Days AC over JRC Pavement at Champaign, Illinois (WF) 79

80 Cracking Extent and Severity: AC over FC over AC, WF % Total Length of Cracks L+M+H M+H H 100/e Number of Days AC over FC over AC Pavement at Frederick, Maryland (WF) 80

81 Cracking Extent and Severity: AC over CRC, WF % Total Length of Cracks L+M+H M+H H (Not calibrated) 100/e Number of Days AC over CRC Pavement at Tippecanoe, Indiana (WF) 81

82 Cracking Extent and Severity: AC over PetroGrid over PCC, WNF % Total Length of Cracks L+M+H M+H (Not calibrated) H (Not calibrated) 100/e Number of Days AC over PetroGrid over PCC pavement at Waco, Texas (WNF) 82

83 Cracking Extent and Severity: AC over PetroGrid over AC, DF % Total Length of Cracks L+M+H M+H (Not calibrated) H (Not calibrated) 100/e Number of Days AC over PetroGrid over AC pavement at Amarillo, Texas (DF) 83

84 Cracking Extent and Severity: AC over AC, WNF % Total Length of Cracks Number of Days AC over AC Pavement at Houston, Alabama (WNF) L+M+H M+H H (Not calibrated) 100/e 84

85 Cracking Extent and Severity: AC over FC over AC, WNF % Total Length of Cracks L+M+H M+H (Not calibrated) H (Not calibrated) 100/e Number of Days AC over FC over AC Pavement at Yazoo, Mississippi (WNF) 85

86 Cracking Extent and Severity: AC over AC, DF % Total Length of Cracks L+M+H M+H H 100/e Number of Days AC over AC Pavement at Deaf Smith COUNTY, Texas (DF) 86

87 Cracking Extent and Severity: AC over AC, DNF % Total Length of Cracks L+M+H M+H H 100/e Number of Days AC over AC Pavement in San Bernardino, California (DNF) 87

88 Cracking Extent and Severity: AC over GlasGrid over PC, WF % Total Length of Cracks Number of Days L+M+H M+H H (Not calibrated) 100/e AC over GlasGrid over PC pavement at New York, New York (WF) 88

89 Flow Chart of NCHRP 1-41 Selected Test Sections 10 models Selected Test Sections Field Data Collection Distress Traffic Pavement Weather Field Data Analysis Distress Traffic Axle load distribution Calibration Process Asphalt modulus Field Data Analysis Falling weight deflectometer Artificial neural network Calculated number of days Thermal (2) Shear (2) Bending (1) Calibration to field distress Five calibration coefficients Three levels of damage Field Data Collection Temperature Model Crack Propagation Model Calibration Process Final Model of Reflection Cracking Temperature Model Weather data Modeling temperature with depth New temperature model Pavement Data Analysis Crack Propagation Model Temperature Traffic shear Traffic bending Artificial neural network models Modulus Stress intensity factors Viscoelastic thermal stress Crack growth Pavement Data Layers Material properties Non-destructive testing 89

90 NCHRP 1-41 Calibration of Fracture Predictions to Observed Reflection Cracking in HMA Overlays Robert L. Lytton Fang-Ling Tsai, Sang-Ick Lee Rong Luo, Sheng Hu, Fujie Zhou Pavement Performance Prediction Symposium July 16, 2009, Laramie, Wyoming