Mechanistic-Empirical Pavement Design Guide

Mechanistic-Empirical Pavement Design Guide Tommy Nantung INDOT Research and Development Division February 13, 2014

Pavements are designed to fail (in a predictable way)

Performance vs. Design Life

From AASHTO 1993

MEPDG Procedure Designer enters a trial design for a given set of site conditions Design software analyzes the trial design and predicts performance Design may be modified until agency performance requirements are met It means, there is no unique solution

MEPDG as a Tool Pavement Design Practical pavement design tool for practitioners Pavement Analysis Pavement analysis Materials acceptance Pavement performance analysis Others Academic exercise

Which one is first? Chicken Correct traffic data Egg Local calibration

Pavement ME

MEPDG DARWin-ME Pavement ME Climate Traffic Materials Structure Damage Response Time Damage Accumulation Distress

M-E Design Process Input Data Traffic Model Environmental Effects Model Pavement Response Model(s) Distress Models Performance Predictions Material Characterization Models

Revise trial design User s Decisions Inputs General Traffic Climate Structure Selection of Trial Design Structural Responses (s, e, d) Damage Accumulation with Time Design Reliability Calibrated Damage-Distress Models Distresses Smoothness Performance Verification Failure criteria Design Requirements Satisfied? No Yes Feasible Design

AASHO Road Test Location Ottawa, IL

Field Performance - The LTPP Study

Rigid Pavement Principle

Importance of Traffic What is an ESAL? (based on serviceability) d e c e t, or s t Which criterion? (they don t all give the same result!)

Traffic Input No More ESALs Number of axles by: Axle type Truck type Axle load interval

Percent slabs cracked Effects of Climatic Conditions 70 60 50 40 17 million trucks (31 million ESALs) 9.5-in slab; 15-ft joint spacing; 4-in CTB 28-day PCC MR = 700 psi α = 6 x 10-6 / F Southern California 30 20 10 Illinois 0 3.4 3.6 3.8 4.0 4.2 4.4 4.6 28-day PCC modulus of elasticity, million psi

Percent slabs cracked Combined Effects of α PCC and E PCC 45 40 35 30 25 20 15 10 5 α = 5.5 x 10-6 / F α = 6.0 x 10-6 / F 0 28-day Epcc = 3.6 million psi 28-day Epcc = 4.2 million psi

Incremental Damage Calculation Time increment PCC Strength CTB Traffic Base Modulus Subgrade Modulus 0 2 4 6 8 Time, years

Stress and Strain in Rigid Pavement Curling stress

JPCP Bottom-up Cracking (Mid-slab Load + Positive Curl/Warp Condition) Base Subgrade Critical stress region at bottom of slab

JPCP Top Down Cracking (Joint Load + Negative Curl/Warp Condition) Critical stress region at top of slab Base Subgrade

Critical Loading Condition Bottom-up cracking Outside Lane Direction of traffic Shoulder Critical location (bottom of slab)

Critical Bottom-up Stresses

Critical Loading Condition Top-down cracking Outside Lane Direction of traffic Shoulder Critical location (top of slab)

Critical Top-down Stresses

JPCP Top-down Cracking Top of slab (crack initiation)

Concrete Properties Design Guide uses Gain Curves to estimate the values of structural properties at any time during the design life for use in mechanistic damage analysis.

Influence of Traffic to Performance Concrete IRI Mid-slab cracking Faulting Asphalt IRI Fatigue cracking Asphalt rutting Total rutting Top down cracking

Gain Ride Quality International Roughness Index (IRI) 2 Speed = 80 km/h 1 (Vertical Distance) Horizontal Distance 0 0.01 0.1 1 10 100 Wavelength, m ( Little Book, 1998)

Joint Faulting in JPCP

Transverse Cracking in JPCP

Punchout in CRCP

Importance of Traffic The most single parameter that influences the pavement design

Traffic in Pavement ME

Remaining Traffic, % Damage Contribution, % Effects of Axle Weight Top-down cracking 100% 90% 10% 9% 80% 70% < 5% of traffic 35% of total damage 8% 7% 60% 6% 50% 5% 40% 4% 30% 3% 20% 2% 10% 1% 0% 14 1618 20 2224 26 2830 32 3436 38 4042 44 4648 50 5254 56 5860 62 6466 68 7072 74 7678 80 0% Tandem axle load, kips

Truck Volume (%) Problems in Traffic Data Truck Class Distribution 50.00 45.00 40.00 35.00 Unclassified trucks 30.00 25.00 20.00 15.00 10.00 5.00 0.00 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C0 Truck Class Planning data: 126,005 AADT WIM actual data: 101,199 AADT

Traffic Data Analysis

Truck Weight Road Groups Indiana has 56 WIM sites, 7 more to come Provide files for: Monthly Adjustment Factor Vehicle Class Distribution Hourly Distribution Axle Load Distribution Groups A for AADTT = 1 to 3,000 B for AADTT = 3,001 to 6,000 C for AADTT = 6,001 to 20,000 D for AADTT > 20,000

Default Traffic Load Spectra Parameter Input Level 1 Input Level 3 AADTT, (Seasonal) Truck type distribution Axle weight distribution Segment specific Segment specific, AVC Segment specific, WIM Not Required Default TTC Default TTC AADT Not Required Segment specific, counts % Trucks Not Required Segment specific, counts Tire Pressures Axle Configurations No. of Axles per Truck Type

Hourly Distribution of Trucks Influences the curling

Traffic Wander Pavement Shoulder x Used to calculate pavement responses & the number of axle load applications over a point for predicting distress & performance Direction of traffic Typical Values X (mean) = 457 mm (18 in) X (SD) = 254 mm (10 in) Mean wheel location Standard deviation Design lane width

Which one is first if your traffic data is not correct? Chicken Correct traffic data Egg Local calibration

Design Features

Joint Spacing As a last resort to reduce the pavement curling stress INDOT suggestions 15, 16, 17, and 18 feet Calculate it based on cost savings Thickness versus D1 joint costs Outside Lane Shoulder Direction of traffic Critical location (top of slab)

Widened slab Slab width is assumed 12 feet Slab width can be 12 to 14 feet Influence the thickness <10 inches The paint stripe is painted at 12 feet width Pavement ME can be used to design pavement <12 feet, adjust the traffic wander

Tied Concrete Shoulder Requires correct input of Load Transfer Efficiency (LTE) Monolithically placed and tied with deformed bars traffic lane and shoulder 50 to 70% LTE Separately placed and tied with deformed bars traffic lane and shoulder 30 to 50% LTE

PCCP Materials

Mix Property Inputs Inputs for concrete mix Cement type Type I or Type II or Type III (select from list) Cement content Definition: Weight of cement per cubic yard of concrete Water-cement ratio Definition: Ratio of water to cement by weight Aggregate type Mineral composition of aggregate (select from list) Input Level 1 2 3

Mix Property Inputs, cont. Inputs for concrete mix Used to predict concrete set temperature Input values Project-specific inputs Typical value is agency-specific Input Level 1 2 3

Mix Property Inputs, cont. Shrinkage inputs Ultimate shrinkage Definition: Shrinkage predicted at a relative humidity of 40% Either user inputs or program calculates Reversible shrinkage Definition: Percentage of ultimate shrinkage that is reversible Typical value: 50% Input Level 1 2 3

Mix Property Inputs, cont. Shrinkage inputs, cont. Time to develop 50 percent of ultimate shrinkage Typical value: 35 days Curing method Curing compound (mostly) Wet curing Input Level 1 2 3

Strength Property Inputs Input Level Compressive Strength (f c) Modulus of Elasticity (E) Modulus of Rupture (M r ) Tensile Strength (f t ) * 1 2 3** Level 1 and 2: Inputs at 7, 14, 28, 90 days, and strength ratio at 20 years * Required only for CRCP design Level 3: Inputs @ 28 days **Require either f c, or Mr, or E and f c, or E and Mr

Strength Property Inputs, cont. Compressive strength, f c Definition: Axial stress at failure under compressive load Test: ASTM C 39 Typical value: 4,500 psi Input Level 1 2 3

Strength Property Inputs, cont. Elastic modulus, E Definition: Ratio of stress to strain when the material is elastic Indicator of deformation characteristics of the material Test: ASTM C 469 Typical value: 4,200,000 psi Input Level 1 2 3

Strength Property Inputs, cont. Modulus of rupture, M r Definition: Bending stress in concrete at failure (under flexural loads) Indicator of tensile strength Test: ASTM C 78 Typical value: 700 psi Input Level 1 2 3

Coefficient of Thermal Expansion Life is like a box of chocolates, you never know what you will get next.

Coefficient of Thermal Expansion

Crushed Stone #8 Drainage Layer

Granular Stone #53 Separation Layer

Soil Treatment

Subgrade Soil

Percent Passing (%) Particle-Size Analysis (ASTM D 422) 100 3in 2in 1in 3/4in No.4 No.8 No.40 No.200 90 80 70 60 50 40 p 4 30 20 p 10 200 0 100 D 10 60 1 Sieve Opening (mm) 0.1 0.01 Input 1 2 3 Level

Atterberg Limits (ASTM D 4318) Plasticity Index = Liquid Limit - Plastic Limit Input Level 1 2 3

Dry Unit Weight (pcf) Moisture-Density Relationship (ASTM D 698, D1557) 130 126 122 118 114 g dmax 110 w opt 5 6 7 8 9 10 11 12 13 14 15 Gravimetric Moisture Content (%) Input Level 1 2 3

Resilient Modulus (NCHRP 1-28A, AASHTO T307)

IRI issue in smoothness model (empirical) IRI = IRI I + 0.8203*cracking + 0.4417*Spalling + 1.4929*Faulting + 25.24*SF Where: IRI I = Initial IRI SF = Site Factor = AGE*(1 + FI)(1 + P 0.075 )/10 6 AGE = pavement age, yr FI = Freezing index, o C days P 0.075 = percent subgrade material passing 0.075-mm sieve

Continuously Reinforced Concrete Pavement

Results

Sensitivity Parameter Roughness Faulting Level 3 Percent Slabs Cracked Modulus of Rupture S NS VS Compressive Strength S NS VS Level 2 Compressive Strength S NS VS 20-year/28-day Ratio S NS VS Level 1 Modulus of Rupture S NS VS Modulus of Elasticity S NS VS 20-year/28-day Ratio S NS VS

Sensitivity Parameter Roughness Faulting Permanent Curl/Warp Effective Temperature Difference Percent Slabs Cracked VS VS VS Joint Spacing VS VS VS Dowel Bar Diameter MS MS NS Pavement Thickness S MS VS Poisson s Ratio MS MS S Coefficient of Thermal Expansion VS VS VS Thermal Conductivity S MS VS

MEPDG as a Tool Pavement Design Practical pavement design tool for practitioners Constructability matters, 0.5 inch precision is excellent Pavement Analysis Materials acceptance Pavement performance analysis Others Academic exercise High precision to 0.01 inch

Questions???