the new and the different - purdue university

M-E Pavement Design Guide

The New and the Different

Guide for Mechanistic – Empirical Design

of New and Rehabilitated Pavement Structures

A product of NCHRP 1-37A


Levels of Knowledge


Levels of Knowledge

I. Unconsciously Incompetent


Levels of Knowledge

I. Unconsciously IncompetentII. Consciously Incompetent


Levels of Knowledge

I. Unconsciously IncompetentII. Consciously IncompetentIII. Consciously Competent


Levels of Knowledge

I. Unconsciously IncompetentII. Consciously IncompetentIII. Consciously Competent IV. Unconsciously Competent

7M-E Pavement Design Guide

• Session outlineCapabilitiesCompare AASHTO Guide to 1-37AInputs• Climate Traffic• ACP PCCP• Unbound materials

ReliabilityCalibration and Testing

The New and the Different


Capabilities

• Wide range of pavement structuresNewRehabilitated

• Explicit treatment of major factorsTraffic – Over-weight trucksClimate – Site specific and over timeMaterials – New and differentSupport – Foundation and existing pavement


Capabilities

• Models to predict change in distress over time

• User establishes acceptance criteria Distresses and smoothness


M-E Design ProcessClimate

TrafficMaterials

Structure

DistressResponseTime

Damage

Damage Accumulation

Iterations


M-E Design Process Climate

TrafficMaterials

Structure


Damage

Damage Accumulation

Iterations


The New and Different

1993 Guide 1-37A GuideOutputs

Time Series Distress and Smoothness Prediction

Structural NumberAnd / or Rigid Pavement Thickness


1-37A Guide Outputs: Flexible

Fatigue Fatigue CrackingCracking

Thermal Thermal CrackingCracking

Longitudinal Longitudinal CrackingCracking

IRIIRIRut DepthRut Depth


Joint FaultingJoint FaultingIRIIRI

PunchoutPunchoutTransverse Transverse CrackingCracking

1-37A Guide Outputs: Rigid


NCHRP 1-37A is an Analysis Program


M-E Design ProcessClimate

TrafficMaterials

Structure


Damage

Damage Accumulation

Iterations



Hierarchical LevelsLevel ThreeLevel TwoLevel One

1993 Guide 1-37A Guide

Single ValueI n p u t

Levels


Hierarchical Levels

Level Source Usage

Three Defaults (Routine projects)

Two Correlations (Routine significant projects)

One Project specific data

(Research, forensics and high level projects)


Design Inputs - Hierarchical Levels

Input levels can be mixed and matched

Damage calculations are exactly the same regardless of design input level


Climatic Data



1993 Guide 1-37A GuideCli

mate

SeasonalAdjustments

Drainage Coefficients

Inputs for EICM

Thermal PropertiesWind SpeedAir TemperatureWater Table DepthSun RadiationPrecipitation


Climatic Inputs

• Identify weather stationPick from 800 sites or specifyCreate virtual by averaging surrounding sites

• Create EICM file• Depth to water table

1 2 3√ √ √

Input Level


Processing EICM Inputs –Asphalt Design

Adjustments:• Unbound

Resilient modulusMoisture content

• AC Hourly temperature profile Thermal crackingRutting


AC Hourly Temperature Profile

-40

-30

-20

-10

0

10

20

30

40

50

6/15/94 6/15/95 6/14/96 6/14/97 6/14/98 6/14/99

TIME

Depth = 0 in. Depth = 3 in. Depth = 6 in.


Processing PCC Climatic Inputs

• EICM used to predict Hourly temperature profile Monthly moisture gradient


Concrete Slab Temperature and Moisture Gradients

Slab wetter on top

Slab dryer on top

Curling Warping


TRAFFIC INPUTS




Traffic

ESALs Axle Load Spectra

Truck SpeedGear/Axle

ConfigurationAxle/Tire SpacingTire PressureTraffic WanderMonthly, DailyDistribution Factors

Truck EquivalencyFactors


Traffic Hierarchical Input Levels

Level 3 Level 3 –– AADT & % trucks with TTC GroupAADT & % trucks with TTC Group

Level 2 Level 2 –– AADTT with Regional /StatewideAADTT with Regional /StatewideAVC & WIM dataAVC & WIM data

Level 1 Level 1 –– AADTT with site specific AVC &AADTT with site specific AVC &WIM dataWIM data


Input LevelInput Parameters

1 2 3Inputs Required to Compute AADTT

AADTT for Base Year √ √AADT and Percent Trucks for Base Year √

Truck Traffic Volume Adjustment Factors

Hourly Distribution Factors √ √ √

Truck Traffic Growth Function/Factor √ √ √

Truck Distribution Factors - Base Year √ √Monthly Distribution Factors √ √ √

Directional Distribution Factor √ √ √Lane Distribution Factor √ √ √

Traffic Module Inputs - Overview


Input LevelInput Parameters 1 2 3

Axle Load Distribution FactorsAxle Load Distribution Factors √ √

Truck Traffic Classification (TTC) Factor √

General Traffic InformationNo. of Axle Types per Truck Class √ √

Axle Spacing √ √

Axle Load Groups √ √ √

Tire Spacing/Axle Configuration √ √ √

Tire Pressure √ √ √

Traffic Module Inputs - Overview


Traffic Module Output Files(Load Spectra)

Load GroupYear Month Hour Axle

Type 0-2 2-4 4-6 .. x-yi j k Single

TandemTridemQuad

A 20 year design will have 5760 hours in this matrix

20 years x 12 Months x 24 Hours


Truck and Axle Load Distribution Factors

• Use Truck Traffic Classification (TTC):Defaults derived from LTPP DataSelect one of the 17 Groups

TTC Selection is based on functional classification and overall distribution of the major truck classes (buses, single unit trucks, single-trailer trucks, and multi-trailer trucks)

1 2 3

√Input Level

34M-E Pavement Design GuideKathy Petros, FHWA 2003

35M-E Pavement Design GuideKathy Petros, FHWA 2003


NCHRP 1-39

Traffic Data Collection, Analysis andForecasting for Mechanistic Design• Developed Software, TrafLoad• Beta version under review

Reads C-card and W-card dataManipulates data into 1-37A formatIntended to supply the traffic needs of 1-37A



Foundation


ResilientModulus

Universal non-linearResilient modulusModel “k” values


Unbound Materials(Aggregates and Subgrade)

• Resilient ModulusLevel 3 DefaultsLevel 2 CorrelationsLevel 1 Materials specific testing

• VariabilityNoneSeasonal ValuesEICM


Unbound Material General Properties

• Unbound Material Type - select from:AASHTO Classification (AASHTO M 145)Unified Soil Classification System(ASTM D 2487)Other (crushed stone, cold recycled AC)

• Layer Thickness: thickness of the layer in inches

1 2 3

√ √ √Input Level


For PCCP

Subgrade resilient modulus is converted to a k-value that produces equivalent surface deflections for each month in year


ASPHALT MATERIAL PROPERTY

AND DESIGN INPUTS


The New and Different -- Flexible Design

1993 GuideHMA

Materials

Layer Coefficient

IDT Resilient Modulus (68°F)

Dynamic Modulus

Poisson’s ratio

1-37A Guide


Mix Dynamic Modulus

• Level 3 – Predictive equation and binder class 2 – Predictive equation and binder tests1 – Laboratory mix tests

• Predictive equationGradationAir VoidsAsphalt contentBinder information


CONCRETE MATERIAL PROPERTY AND DESIGN

INPUTS


The New and Different-- Rigid Design

1993 Guide 1-37A GuideModulus, FlexStrength, TensileStrength(28-day)

Modulus of Elasticity(7, 14, 28 & 90 day)

Flexural, TensileStrength

Poisson’s ratio

PCC Thermal PropsDrying ShrinkageCoefficient of ThermalExpansion

PCC

Materials


CRCP Design Features Inputs

• ReinforcementBar diameterSpacingPercent steel

• Base propertiesBase typeErodibility Base/slab friction coefficient

• Crack spacing (optional)

1 2 3



JPCP Design Features Inputs

• Joint DetailsJoint spacingSealant typeDowel diameter and spacing

• Edge SupportShoulder type and LTEWidened slab

• Base propertiesBase typeInterface type, i.e. bonded or unbondedErodibility

1 2 3



Performance Evaluation

• Procedure evaluates the trial design to determine if it meets the desired performance criteria at individually set reliability levels

Trial design


New Approach to Design Reliability

• Different than AASHTO 1986/93• Based on predicted distress and IRI • User selects reliability levels and

performance criteria for distress and IRI


Design Reliability

• Based on the “residual” of prediction of the individual distress models

• These “residuals” represent the knowledge that exists of the accuracy of the distress prediction models


Reliability

As proposedProbabilistic approachMonte Carlo simulation

As DeliveredVariability of predicted vs observedCalibrated to national LTPP data (Level 3)


Pavement Performance

Terminal Serviceability IndexCon

ditio

n

Time




ditio

n

Time




ditio

n

Time

50%




ditio

n

Time

60%

50%




ditio

n

Time

80%

60%

50%




ditio

n

Time

50%

60%

80%

90%


Normal Distribution

Probabilistic Approach

Mean Value


Simulation Modeling

PredictedCracking

PredictedDistress

Inputs DistressModel

y= f(x)

Loadings

Climate

Materials


Level 3

Hierarchical Inputs


Level 2

Hierarchical Inputs


Level 3

Level 1

Hierarchical Inputs


Simulation Modeling - Level 3

PredictedCracking

PredictedDistress


y= f(x)

Loadings

Climate

Materials


Simulation Modeling - Level 1

PredictedCracking

PredictedDistress


y= f(x)

Loadings

Climate

Materials


Simulation Modeling

y= f(x)

PredictedCracking

PredictedDistress


y= f(x)

Loadings

Climate

MaterialsLevel 3

Level 1


Reliability

As proposedProbabilistic approachMonte Carlo simulation

As DeliveredVariability of predicted vs observedCalibrated to national LTPP data inputs(Level 3)Based on national calibration/LTPP


Variability

Predicted

Obs

erve

d


Variability

50%

Predicted

Obs

erve

d


Variability

75%

50%

Predicted

Obs

erve

d


Variability

25%

75%

50%

Predicted

Obs

erve

d


Variability

25%

75%

50%

Predicted

Obs

erve

d 90%


Variability

25%

75%

50%

Predicted

Obs

erve

d 90%

10%


Variability

Predicted

Obs

erve

d

50%90%

Easily meet 90% Confidence Limit

Criteria Limit


Variability

Predicted

Obs

erve

d

50%90%

Fails to meet 90% Confidence Limit

Criteria Limit


Assessment of Reliability

Time

IRI

IRI0

IRIavrg

Probability of failure (α)

IRIfailure

ReliabilityR = 1 - α

IRI level for Reiability R

Expected IRI

Pavement Life Time

IRI

IRI0

IRIfailure

IRI level for Reliability R

Pavement Life

Predicted IRI R = 50%


Assessment of Reliability

Time

IRI

IRI0

IRIavrg


IRIfailure


IRI level for Reiability R

Expected IRI

Pavement Life Time

IRI

IRI0

IRIavg


IRIfailure


IRI level for Reliability R

Predicted IRIR = 50%

Pavement Life


Comments on reliability

See notes below


1-37A Guide Calibration

• Done with national LTPP data

• Default values also from LTPP

• Confirm/change national defaults

• NCHRP 1-40 Guidelines for local calibration (with FHWA workshops to follow)


Implementation – Calibration

• Requires extensive experimental studies, including:

Field testing programsLaboratory testingData analysis


Field Testing Programs

• Select agency test sites (LTPP and others) that includes the entire range of:

Climate types and areas in the agencyPavement types• AC (all types), PCC (all types)• Types of overlays and rehabilitation

alternatives• Base and subgrade types• Joint types in PCCTraffic characteristics


Field Testing Programs, Cont.

• Obtain pavement performance data

Distress surveysFWD and core testingPavement profileMaterial related distresses

• Determine in-place material properties


Laboratory Testing

• Extract cores

• Determine properties of in-situ material

• Calibration test are the same as those performed for new designs


NCHRP 9-30

NCHRP 9-30 “Experimental Plan for Calibration and Validation of HMA Performance Models for Mix and Structural Design”

• Set up a national database for HMA calibration

• Initially populated with NCHRP 9-19 and LTPP data

• NCHRP 9-30A to populate database for missing material types


Data Analysis

Local calibration will involve recalibrating the distress models using data collected from the selected local sections


Regional/Local Calibration Process

βs = Agency Calibration Factor

Calibrated National Predicted Performance

Act

ual F

ield

Per

form

ance

βs


Summary

• Covered 1-37A capabilities, inputs, reliability, and calibration

• Compared AASHTO Guide to 1-37A

• Described local calibration process

• Detailed 1-37A inputs discussed further in the Implementation session


67 kN15,000 lb

0.48 ESAL

27 kN6,000 lb

0.01 ESAL+ = 0.49 ESALs

151 kN34,000 lb

1.10

151 kN34,000 lb

1.10++ ==

54 kN12,000 lb

0.19++ 2.39 ESALs

J.Q. Public Produce

No More ESALs !

ESAL = ESAL = (ADT)(ADT)00(T)(T(T)(Tff)(G)(D)(L)(365)(Y))(G)(D)(L)(365)(Y)

Bulldog Trucking


Traffic Load Spectra

• Key component of the new AASHTO Design Guide

• Load spectra is the distribution of the number of axles by load ranges for different axles (single, tandem, tridem, quad) for various vehicle classes.

Distribution by time (e.g., concrete pavement distresses greatly influenced by hourly traffic distribution)


Logging Trucks, Olympic Peninsula, 1947

UW

Libraries


ACCURACY

Kathy Petros, FHWA 2003

M-E Pavement Design Guide Kathy Petros, FHWA 2003

the new and the different - purdue university

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