2002 Pavement Design

Transcription

1 2002 Pavement Design Federal Highway Administration June 2001 Thomas P. Harman Asphalt Team Leader

2 Predicting Pavement Performance Pavements are designed to fail But how do they perform?

3 Defining Performance Structural vs. functional Structural: load carrying capacity Functional: ride quality and safety Associated failures Load: caused by traffic Non-load: caused by climate, materials, and construction

4 Performance Modeling Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

5 Our Project State of Confusion 10 mile section 36 feet wide

6 Our Project Materials Environment Traffic Modeling Performance Prediction

7 Material Characterization Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

8 Materials 2 layer system Glooptonyte Base

9 Materials Characterization Response to... Load

10 Materials Characterization Response to... Load, Temperature

11 Materials Characterization Response to Load, Temperature, Moisture

12 Materials Characterization Response to Load, Temperature, Moisture, & Time.

13 Glooptonyte All Glooptonyte is the same It is homogeneous, isotropic, and elastic It is not effected by moisture It is not effected by time

14 Glooptonyte 100 Stress (Load/Area) E 20 C 0 C Strain (Length/Length)

15 Material Characterization Model Modulus, E = Stress / Strain Predictive Model Strain = Stress / E Stress Does our model work? Strain

16 Environmental Effects Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

17 Glooptonyte 25 Modulus, E C 20 C Temperature

18 Environmental Effects Model Modulus, E = (T) T is the temperature in C Does our model work? C 20 C

19 Testing the Model E = (T) At T = 10 C E predicted =? Modulus, E C 10 C 20 C Temperature

20 Inputs Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

21 Environmental Conditions 30 Temperature, C Winter Spring Summer Fall Pavement Season

22 Environmental Effects Model Glooptonyte Modulus, E E = (T) Winter Spring Summer Fall

23 Traffic No more ESAL s Traffic input by Vehicle type Axle weight Load Spectra

24 Traffic Conditions State of Confusion Unicycles only Two loadings

25 Traffic Conditions Category A Unicycle Load, P 750 lbs Pressure, p 60 psi Contact, a 2 in Category B Unicycle Load, P 1250 lbs Pressure, p 100 psi Contact, a 2 in a, contact radius p = P / ( п r 2 )

26 Traffic Conditions Category A AADT 600,000 Category B AADT 400,000 AADT, (millions) No No Anticipated Growth Total Cat A Cat B Time, years

27 Design Life 20 years Total anticipated traffic: Category A = 12 million Category B = 8 million TOTAL = 20 million

28 Pavement Response Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

29 Pavement Response

30 To Load

31 Pavement Response Tools Analytical solutions (e.g. Burmister) Multi-layer elastic theory Finite element analysis 2D, 3D Hybrid methods

32 Analytical Solution (Burmister) Assumptions µ = 0.5 (Poisson s ratio) E BASE = 1 / 10 E Glooptonyte Surface deflection = 1.5 p a F 2 E BASE F 2 = 2 f f (a, (a, E 2 / / E 2 1, 1, t t 1 ) 1 )

33 Burmister 1.5 p a F 2 E BASE Glooptonyte, t = 8 inches Category A Unicycle, p = 60 psi, a = 2 Summer time, T = 20 C E 1 = (T) = 10 ksi E 2 = 1 / 10 E 1 = 1 ksi = 1000 psi F 2 = f (a, E 2 / E 1, t 1 ) = 0.20 (from a Table) Surface Deflection = 0.04

34 Pavement Sections 4 inches 6 inches 8 inches

35 Pavement Response Model Season E 1 / E 2 psi Category A p = 60 psi Category B p = 100 psi Winter / Spring (T p = 0 C) 20 / 2 δ 4 = 0.03 δ 6 = δ 4 = δ 6 = δ 8 = 0.02 δ 8 = 0.03 Summer / Fall δ 4 = 0.06 δ 4 = 0.09 (T p = 20 C) 10 / 1 δ 6 = 0.05 δ 6 = δ 8 = 0.04 δ 8 = 0.06

36 Distress Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

37 Only fails in rutting Glooptonyte

38 Pavement Distress Model Empirical Mechanistic Mechanistic-Empirical

39 Empirical Model Pavement Response Traffic Loading Pavement Performance

40 Empirical Model Log ( δ P / δ B ) = C 1 + C 2 Log (N) Where: δ P, Permanent deformation δ B, Burmister deflection C 1 and C 2, Constants (-3.1, 0.5) N, load applications

41 Performance Criteria What is acceptable performance? Rutting < 0.3 inches

42 Performance Prediction Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

43 Empirical Model Session Cat t δ B N design δ P W / S A ,000, S / F A ,000, W / S B ,000, S / F B ,000, Log ( δ P / δ B ) = C 1 + C 2 Log (N) Rutting

44 Performance Prediction 0.40 Rutting, inches Prediction Criterion Glooptonyte Thickness, inches

45 Performance Prediction Input Data Environmental Effects Models Pavement Response Model 8 Distress Model Performance Predictions Material Characterization Model

46 Models Calibration Log ( δ P / δ B ) = β 1 C 1 + β 2 C 2 Log (N) Where: β 1, β 2 calibration factors

47 Models Calibration Measured Performance * * * * * * * * * * * * * Predicted Performance * * β 1, β, 2 National Then Local

48 Performance Modeling Input Data Environmental Effects Models Pavement Response Model Distress Model Performance Predictions Material Characterization Model

49 Thank You