Seminar on Pavement Design System and Pavement Performance Models Reykjavik, 22. 23. March, 2007 Mechanistic Pavement Design A Road to Enhanced Understanding of Pavement Performance Sigurdur Erlingsson Dept. of Civil and Env. Engineering University of Iceland Iceland & Dept. of Highway Engineering VTI Sweden
Outline The Problem Current Design Methods Mechanistic-Empirical Design Methods Important Factors Influencing Pavement Performance Traffic Loading Material Characteristics The Climatic Conditions and Seasonal Variation of Pavement Response Response Calculation and Distress Prediction Validation Accelerated Testing of Pavement Structure Conclusion
The Problem Distress Mechanisms Fatigue Cracking Longitudinal Cracking Roughness Rutting Thermal Cracking
Current Design Methods Relay on empirical correlations with past performance. Based on 1950 s AASHTO Road Test data. Index value based characterization R-value CBR-value They are obscure and difficult to apply in new situations.
Critical stress and strain locations Load 50 kn, φ = 300 mm Asphalt Unbound or Bound Base Subbase Subgrade 1. Tensile strain at pavement surface. 2. Tensile strain at bottom asphalt. 3. Compressive stresses in top unbound base. 4.Tensile strain at bottom bound base 5. Vertical compressive strain at top subbase. 6. Vertical compressive strain at top subgrade. Use linear elastic multi layer system, so characterise materials with E and μ. Assume full adhesion between the layers. Use static load(s). Use transfer functions (fatigue relations) to calculate pavement life. Drawbacks: materials are NOT linear elastic. They are non linear elasto-visco viscoplastic and often rate, temperature and moisture dependent.
Mechanistic-Empirical Design Mechanistically calculate pavement response (i.e., stresses, strains, and deflections) due to: Traffic loading Environmental conditions Accumulate damage over time Empirically relate damage over time to pavement distresses, e.g.: Cracking Rutting Faulting Calibrate (validation) predictions to observed field performance
Mechanistic-Empirical Design Climate Materials Traffic Structure Damage Response Time Damage Accumulation Distress
Incremental design procedure Flow diagram i = 0 t = 0 1. Initial Condition and Structure 2. Geometry 3. Traffic and Loads 5. Climate and Enviroment i = i+1 t i+1 = t i +Δt 6. Response Model 7. Stresses, Strains, Displacements 9. Structural Change ΔD 4. Material Properties 8. Performance Model 10. Current Condition Σ(ΔD) 11. History of Pavement Damage
Design criteria -
Factors Influencing Performance and Distress Development Traffic Loading Material Characteristics Climatic Conditions and Seasonal Variation of Material Properties
Traffic loading
Dual wheels, super super single and super single 20 cm 50 cm 34 cm
Contact pressure distributions Vertical Lateral pressure distribution
Axle Load Spectrum - Weigh in Motion WIM-stations provide information on: Axle loads Number of load repetitions Frequency distribution F(t)
Example of a Axle Load Spectrum Axle Load (kn) Single Number Tandem of Axles Tridem Quad 50-70 3.000 200 60 5 70-90 1.000 1.000 300 10 90-110 100 3.000 600 30 110-130 30 2.000 800 80 130-150 4 1.000 1.000 100 etc
Material Properties - Dynamic testing Dynamic testing simulates field conditions better than static testing, therefore a better correlation is expected with field performance. Layer Test method Property Asphalt Concrete Triaxial Testing Indirect Tension Test Uniaxial Compression Bending Test Stiffness, perm def. Stiffness, Fatigue Creep Fatigue Bitumin.stab. Base Course Triaxial Testing Indirect Tension Test Uniaxial Compression Stiffness Stiffness, Fatigue Creep Unbound granular materials Triaxial Testing Stiffness, Permanent Deformation Behaviour
HMA Mixture: Dynamic (Complex) Modulus E * = σ ε 0 0 Adjusted for temperature & time of loading. E* = Dynamic modulus σ o ε o = Maximum (peak) dynamic stress = Peak recoverable axial strain Phase lag Stress Strain Time φ = t t i p ( 360)
HMA - Material Properties Indirect Tension test Load W Time Deformation Time AC σ y ε x Base course Subbase Subgrade
Uniaxial Compression Test Load Perm. def. W Time Number of load pulses AC σ y ε y Base course Subbase Subgrade
Bending test log N Field fatigue Lab fatigue Shift factor (healing( healing, lateral wander, damage propagation, stress redistribution etc. 2.5-40) 4 p bending 2 p bending log ε
Unbound Granular Materials Repeated Load Triaxial testing Stiffness - M r, ν (nonlinear behaviour). Permanent deformation behaviour Stiffness Perm. def. Mean stress level Number of load pulses
Climatic conditions and seasonal variations
Temperature & Resistivity Probe
Environmental data for section 1.4.2 20 15 10 5 0 Non-frost Unfrozen moisture during winter Winter thaw Unfrozen moisture during winter Spring thaw 1/10/99 31/10/99 1/12/99 31/12/99 31/1/00 1/3/00 1/4/00 Gravimetric moisture cont. [%] 15 Grundartangi 10 5 0-5 -10 1/10/99 1/11/99 1/12/99 1/1/00 1/2/00 1/3/00 1/4/00 Temperature [ C]
Moisture Content vs. Time & Depth 20 Temperature [ C] 15 10 5 0-5 -10-15 Air temperature 15.3.02 31.3.02 16.4.02 2.5.02 18.5.02 3.6.02 Base course Subbase Subgrade March, 15 th 2002
Frost Resistivity Probe 120 Relative conductivity [% 100 80 60 40 20 0 Sensor 1, d = 5 cm Sensor 2, d = 10 cm 1 3 5 7 9 11 13 15 17 19 21 23 No. measurements
Vol. Moisture cont. and rel. conductivity 0.8 Vol. Moisture cont [-] Rel. Conductivity [%] Vegraki [-] Hlutfallsleg rafleiðni 0.6 0.4 0.2 0.0 100 80 60 40 20 d = 7 cm d = 17 cm d = 42 cm d = 57 cm d = 95 cm d = 119 cm 1.1.06 31.1.06 2.3.06 1.4.06 1.5.06 31.5.06 30.6.06 d = 10 cm d = 15 cm d = 40 cm d = 55 cm d = 110 cm 0 1.1.06 31.1.06 2.3.06 1.4.06 1.5.06 31.5.06 30.6.06
Climatic data - Vatnsskard April 4 th, 2002 Temerature [ C] Rel. Conductivity [-] Norm. vol. moisture cont. [-] 0-5 -2,5 0 2,5 5 0 0 50 100 0 0,0 0,5 1,0 1,5 2,0 20 20 20 Depth [cm] 40 60 Dýpi [cm] 40 60 Depth [cm] 40 60 80 80 80 100 100 100 120 120 120
Climatic data - Vatnsskard April 16 th, 2002 Temperature [ C] Rel. Conductivity [-] Norm. vol. mositure cont. [-] 0-5 0 5 10 15 0 0 50 100 0 0,0 0,5 1,0 1,5 2,0 Depth [cm] 20 40 60 80 Depth [cm] 20 40 60 80 Depth [cm] 20 40 60 80 100 100 100 120 120 120
Falling Weight Deflectometer tests r 0 r 1 r 2 r 3 r 4 r 5 F D 0 D 1 D 2 D 3 D 4 D 5
FWD backcalculations Base Course - Depth 0-20 cm Stiffnesses 100000 E1 [MPa] 10000 1000 100 1.jan 31.jan 2.mar 1.apr 1.maí 31.maí 30.jún 30.júl 29.ágú Subbase - Depth 20-60 cm 100000 E2 [MPa] 10000 1000 100 1.jan 31.jan 2.mar 1.apr 1.maí 31.maí 30.jún 30.júl 29.ágú
Seasonal Variation of Stiffness in Unbound Layers Stiffness WINTER SPRING SUMMER AUTUMN Subgrade Base Course
Validation Full scale testing Accelerated Pavement Testing Purpose Heavy Vehicle Simulator To increase the understanding of pavements performance under heavy loading conditions.
The Pavement Structures (IS02 & IS03) IS 02 IS 03 0 30 Surface dressing, 2 layers, 12-16/8-12 mm crushed aggregate 0 30 Surface dressing, 2 layers, 12-16/8-12 mm crushed aggregate, 30 mm Unbound base, 0-25 mm crushed aggregate 130 Bitumen stabilized base, 0-25 mm crushed aggregate Unbound base, 0-25 mm crushed aggregate 230 230 Subbase, 0-75 mm aggregate Subbase course, 0-75 mm aggregate 430 Subgrade, sand 430 Subgrade, sand z [mm] z [mm]
Response Testing - Numerical Simulations 2-D Axi & 3-D analysis. MLET & FEM analyses Linear and non-linear base behaviour Distress prediction
IS02 Single wheel, p = 800 kpa FEM: Vertical displacement
IS02 Vertical Stresses vs. Depth Stress σ z [kpa] 0 200 400 600 800 1000 W = 120 kn 0 Single wheel Dual wheel p = 900 kpa 10 0.0 Surface dressing 1.2 20.3 39.7 Depth [cm] Unbound base course 20 Subbase 30 Subgrade 40 Profile 1 Profile 2 Profile 3 Depth [cm] 50 Measurements 3D FEM LE 2D Axi MLET LE 2D Axi MLET NLE 2D Axi FE LA 2D Axi FE NLE
Conclusions Mechanistic - empirical based design methods are under development in many countries and will therefore probably be in use in the near future. To be able to use such methods we need to obtain information for modelling purposes on factors affecting pavement performances, such as Axle loading Material properties Weather and environmental conditions Further we need information to calibrate and validate such methods if acceptable agreement between real performance and our estimation is to be achieved.
Conclusions cont. What will we gain Far more realistic pavement characterization Better understanding of pavement performances Effects of new loading conditions such as increased loads, higher tyre pressure and multiple axle, can easily be estimated Future enhanced or improved knowledge can be easily implemented