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Diversos Aspectos de la Implementacion de la Guia de Diseño Mecanistico- Empirico (MEPDG) en Texas Dr. Jorge A. Prozzi The University of Texas at Austin Valparaiso, Chile, 10 November 2010

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Local Calibration of the Permanent Deformation Performance Models Seasonal Time-Series Models for Supporting Traffic Input Data Effect of WIM Measurement Errors on Load- Pavement Impact Estimation Variability in Pavement Design and Its Effects Improving the Roughness (IRI) Predictions by Correcting for Possible Bias Presentation Outline

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Local Calibration of the Permanent Deformation Performance Models for Rehabilitated Flexible Pavements Ambarish Banerjee Jose Pablo Aguiar-Moya Dr. Andre de Fortier Smit Dr. Jorge A. Prozzi

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Outline Background The MEPDG LTPP SPS-5 Analysis Inputs Objectives and Approach Results Specific Conclusions

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Historical Background Standard for Pavement Design in most regions of the USA is the AASHTO 1993 Design Guide, which is an empirical method Primarily based on results from the AASHO Road Tests conducted in late 1950s, early 1960s –Materials used for surface, base and subbase layers were uniform throughout the test –Test conducted in one location (soil, environment) –Low levels of traffic (about 8 million ESALs max.)

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Historical Background

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Deficiencies in the AASHTO Design Procedure –Results from the AASHTO method cannot account for different geographical locations –AASHTO method somewhat antiquated based on today's construction practices and materials –Loads seen by pavements today are much greater resulting in large extrapolations –Mechanical-Empirical methods have gained increasing popularity

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The MEPDG Mechanistic-Empirical Pavement Design Guide (MEPDG) is an analysis tool –Sponsored by the AASHTO Joint task Force on Pavements –Assumes pavement is a layered structure with each layer exhibiting elastic properties –Like AASHTO method uses “national averages” that need to be calibrated

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Input Levels Three input levels: –Level 1: Highest level of accuracy used for site specific design –Level 2: Intermediate level and can be used for regional design –Level 3: Least accurate and can be used on a state level

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LTPP Database Long Term Pavement Performance Database –Established in 1987 as part of SHRP –Monitors both in-use, new and rehabilitated pavement –Created a national database to share and compare data –General Pavement Studies (GPS) –Specific Pavement Studies (SPS)

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LTPP Database GPS –Studies on pre-existing pavements, one section at each location –In-service and have a common design located throughout the USA and Canada SPS –To study the effects of specifically targeted factors –SPS-5: Rehabilitation of Asphalt Concrete Pavements

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SPS-5 Experimental Design Eight or nine sections at each location (depending on availability of control section) Factors Studied: –Overlay Thickness: Thin vs. Thick (> 5 inches) –Surface Preparation: Milling vs. No Milling –Type of Asphalt Mixture: Virgin vs. RAP

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Analysis Inputs - General LocationMonitoring Start Overlay Const. Opened to Traffic AADTT Growth Rate (%), Linear Analysis Period (yrs) New JerseyNov ’91Jul ’92Aug ’928405.914 ColoradoJan ’87Sep ’91Oct ’917992.49 MissouriJan ’98Aug ’98Sep ’985693.18 MontanaJan ’87Sep ’91Oct ’917024.510 TexasJan ’87Sep ’91Oct ’9130116.114 OklahomaJan ’87Jul ’97Aug ’972924.010

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LTPP SPS-5 sections

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Analysis Inputs - Traffic Data available from counts, automatic vehicle classification (AVC) systems and WIM stations Estimation of initial traffic and growth rate

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Analysis Inputs – Vehicle Class Vehicle class distribution at each of the six SPS locations

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Analysis Inputs – Axle Spectra Default values for each axle type, vehicle class and month are already provided Site specific axle spectra for each month and vehicle type was generated after averaging over the number of years in the monitoring period

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Seasonal Variation in Axle Spectra Axle Spectra for NJ SPS-5 location for January Axle Spectra for NJ SPS-5 location for February

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Analysis Inputs – Material New Jersey section 0-502, No milling LayerTypeMaterial Thicknes s Modulus (psi) Binder Grade Binder Content (%) Air Voids (%) 1 Asphalt HMA1.9”AC 408.17.3 2 Existing HMA 2.7”AC 3010.03.6 36.2”AC 107.72.7 4 Granular Base A-1-b 5.2” 26500 520.5” 6SubgradeA-2-4semi-inf21500 Gradation for both asphalt and unbound layers were also available Atterberg’s limits, MDD and OMC was available for unbound layers

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Objective Determination of Level 2 bias correction factors for rehabilitated pavements for the permanent deformation performance models.

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Approach Performance data available from the SPS-5 sections will be compared to predicted pavement performance from the MEPDG Bias correction factors are adjusted to reduce difference between the observed and predicted values

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AC Rutting Transfer Function H ac = Total AC thickness (inches) ε p = Plastic Strain (in/in) ε r = Resilient Strain (in/in) T = Layer Temperature N = Number of Load Repetitions k z, k 2, k 3 = Laboratory Constants β r1, β r2, β r3 = Calibration Coefficients

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Methodology β r1 is a shift factor –Governs the initial rut depth β r3 accounts for the bias due to the number of load repetitions –Slope of the transfer function β r2 is the bias correction factor for temperature susceptibility of hot mix asphalt –Not calibrated due to unavailability of data

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Level 2 Bias Correction Factors CountyStateClimateβ r1 β r3 β s1 Standard Error (in) % Reduction LincolnColorado Dry Freeze 2380.1420.30.05562 Sweet Grass Montana3200.1380.30.10561 Monmouth New Jersey Wet Freeze 1120.1220.70.05525 TaneyMissouri1290.1400.70.08341 KaufmanTexas Wet No Freeze 80.00.4440.50.07559 ComancheOklahoma1070.2520.40.08150

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Comparison of Results Calibrated V/s Uncalibrated Predictions (Section: 08-0502, Colorado)

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Comparison of Results Calibrated V/s Uncalibrated Predictions (Section: 48-A502, Texas)

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Comparison of Results Calibrated V/s Uncalibrated Predictions (Section: 30-0509, Montana)

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Conclusions Level 2 bias correction factors for rehabilitated pavements were proposed Significant differences with new pavements More test sections are needed to improve the confidence in the bias correction factors Validation of bias correction factors is currently being done

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Alguna Pregunta?

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Seasonal Time Series Models for Supporting Traffic Input Data for the Mechanistic- Empirical Design Guide Feng Hong Jorge A. Prozzi

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Outline Introduction Objective of this Study Time Series Models Data Source Case Study Implication Conclusions

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Introduction Pavement design approach: E or M-E Traffic components for pavement design and analysis Traffic load ESAL Load spectra Traffic volume Predicted traffic growth (long-term) Seasonal variation (short-term) Others

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Traffic Input in M-E Guide

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Objectives of This Study Facilitate traffic volume input required by MEPDG Develop mathematical model to incorporate both truck volume components Long-term growth trend Short-term variation Investigate class-based truck volume statistical characteristics

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Seasonal Time Series Model Additive decomposition model Trend component Seasonal component

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Seasonal Time Series Model Linear growth plus seasonality Compound growth plus seasonality

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Model Estimation Approach Linear growth + seasonality model Ordinary Least Square (OLS) Compound growth + seasonality model Nonlinear Least Square (NLLS)

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Available Data Source Nation level: Long-Term Pavement Performance: so far 20 years of records State level traffic monitoring program California: over 100 WIMs Texas: counts, AVCs, 20 WIMs Other resources PMS, freight database, e.g., TLOG

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Case Study Data Used Location: Interstate Highway 37, Corpus Christi, Texas Equipment: Weigh-in-Motion Duration: Jan. 1998 – May. 2002

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Model Estimation Results

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Observed Vs. Predicted Traffic (2)

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Observed Vs. Predicted Traffic (1)

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Further Implication Integrating long- and short- term traffic information

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Conclusions Linear or compound plus time series model is capable of capturing traffic growth trend and seasonal variation accurately Traffic seasonal variation is statistically significant, hence, it should be accounted for Two harmonics are sufficient for representing seasonality One harmonic may be used for simplicity

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Conclusions Both traffic growth and seasonality differ among varying truck classes Short- and long-term traffic information can be effectively and efficiently integrated to accommodate volume input required by MEPDG

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Alguna Pregunta?

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Effect of Weigh-In-Motion System Measurement Error on Load-Pavement Impact Estimation Feng Hong Jorge A Prozzi

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Outline Background –Traffic data collection –WIM measurement error Dataset –Data source –Statistical characteristics Methodology –Load-pavement impact –Incorporating measurement error Conclusions

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Introduction Pavement design inputs –Soil and material properties –Environmental conditions –Traffic load Empirical approach: ESALs Mechanistic-empirical approach: axle load spectra

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Traffic load data collection Static scale –Limited sample size –Accurate Weigh-in-Motion (WIM) scale –Continuous data collection –Accuracy?

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WIM classification Based on sensor technology –Load cell –Bending plate –Piezo-electronic Accuracy Cost

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WIM measurement error Percentage difference WIMWeight : weight measured by WIM scale StaticWeight : weight measured by static scale (assumed to be real weight)

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Measurement error types Random error –An indicator of WIM system accuracy –Intrinsic: equipment design (sensors) –Means of improvement: via manufacturer Systematic error –persistent measurement shift –External: roadway, vehicle & environmental fcts. –Means of improvement: calibration

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Random error

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Systematic error

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Data Source Texas 21 WIM stations

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Axle types SingleTandemTridem

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Axle Load Spectra Single axle Tandem axle

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Statistical Characteristics

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Load-pavement impact Load equivalency factor Load spectra factor (discrete) Load spectra factor (continuous)

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Load-pavement impact under random error: derivation

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Load-pavement impact under random error: result

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Load-pavement impact under both errors: derivation

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Load-pavement impact under both errors: result

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Sensitivity analysis

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Comparison with FHWA-RD-98-104 results

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Summary Investigate axle load spectra statistical characteristics Establish WIM error’s effect on load- pavement impact estimation –Both errors affect result –The result is more sensitive to systematic error Application –Pavement life estimation –WIM equipment selection

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Alguna Pregunta?

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Variability in Pavement Design and Its Effects on the Performance Predictions of the MEPDG José P. Aguiar-Moya Dr. Jorge A. Prozzi Dr. Lance Manuel

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Introduction Variability in Pavement Design Variability Analysis –Pavement Layer Thickness –Asphalt Binder Content –Air Void Content –Modulus of Unbound Material Layers –Modulus of HMA Layers Effect of Variability on MEPDG Predictions Conclusions Presentation Outline

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Many sources of variability have an impact on pavement field performance: –Material properties –Environmental conditions –Traffic loading –Structural layout –Construction practices Effect on Reliability (fiabilidad, confiabilidad) Prior knowledge on the variability of the factors affecting the performance is required! Introduction

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Treating all the variables in a complex analysis procedure, such as MEPDG, is unfeasible. A reduced set of variables has been used in the analysis: –Climatic region –Truck Traffic Classification (TTC) –Average Annual Daily Truck Traffic (AADTT) –Thickness of the HMA layer –Asphalt binder content –Air void content –Thickness of the base –Resilient modulus of the HMA layer –Modulus of the base –Modulus of the subgrade Variability in Pavement Design

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Skewness-Kurtosis Test –Pools the skewness and kurtosis of the distribution into a χ 2 statistic, and compares it to that of a normal distribution where the values are 0 and 3 respectively. Shapiro-Francia Test –Function only of the expected order statistics. –Allows for evaluating normality based on small samples (n≥4) Goodness-of-Fit Tests to evaluate Data Distribution

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Darter et al. (1973) quantified this variability in Standard Deviation (SD) as –HMA layers (0.41 in) –Cement-treated bases (0.68 in) –Aggregate bases (0.79 in) –Aggregate subbases (1.25 in). –The average Coefficient of Variation (CoV) was 10%. Selezneva et al. (2002) and Jiang (2003) studied layer thickness using pavement elevation data from LTPP. –86% of the analyzed layers follow a normal distribution –Mean CoV for asphalt layers around 10%. Variability in Pavement Layer Thickness

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Unfortunately LTPP contains few core / elevation data observation for each pavement section. Use GPR Data LTPP contains GPR data for selected SPS sections. For each section: 600 layer thickness measurements along lane centerline and right wheelpath. Nearly continuous thickness observations for each section Variability in Pavement Layer Thickness

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Skewness-Kurtosis goodness-of-fit tests were performed to assess normality of the data: –99% confidence level was selected –It was found that 88.5% of the HMA surface layers and 80.0% of the granular base layers follow a normal distribution Variability in Pavement Layer Thickness

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Increases in binder content are associated with increased resistance to cracking, but reduced resistance to permanent deformation in the asphalt layers. Prozzi et al. (2005) assumed that the asphalt binder content follows a normal distribution. Hall and Williams (2002) showed that: –Asphalt binder content –Air void content –VMA –Field density Variability in Asphalt Binder Content Follow normal distributions

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Detailed asphalt binder content information was collected for the LTPP SPS-9 sections. SPS-9 was designed to evaluate the performance of Superpave asphalt mixtures. 81 SPS-9 sections were queried from the LTPP –For each of the SPS-9 the number of asphalt binder observations ranged from 24 to 50 Variability in Asphalt Binder Content

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Skewness-Kurtosis goodness-of-fit tests were performed to assess normality of the data: –99% confidence level was selected 85.2% of the HMA layers have asphalt content distributions that follow a normal distribution. The CoV for the analyzed asphalt layers was found to be 0.063 on average (0.009 - 0.392). Variability in Asphalt Binder Content

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The asphalt binder content is closely related to the compaction effort applied during construction, and therefore is also related to the density of the asphalt mix. LTPP contains air void content information for all the flexible SPS sections and for many of the GPS sections. 194 LTPP sections were queried from the LTPP –For each of the LTPP section the number of asphalt binder observations ranged from 6 to 17 Variability in Air Void Content

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Shapiro-Francia goodness-of-fit tests were performed to assess normality at 99% confidence level –98.8% of the HMA layers have air void content distributions that follow a normal distribution. The CoV for the analyzed asphalt layers was found to be 0.051 on average (0.009 - 0.390). Negative correlation between the asphalt binder content and the air void content of -0.175 was found. Variability in Air Void Content

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The modulus of the supporting layers is required in determining the response of a pavement structure. LTPP contains modulus of unbound material layers for all flexible SPS sections and for many of the GPS sections. Information from 1087 untreated subgrade layers and 16 untreated base was identified Variability in Modulus of Unbound Layers

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Shapiro-Francia goodness-of-fit tests were performed to assess normality at 99% confidence level –99.5% of the untreated subgrade layers and for 100.0% of the untreated base layers follow a normal distribution. The CoV for the base layers was on average 0.101 (0.009 - 0.390), and for the subgrade layers 0.093 (0.008 – 0.896). There is positive correlation between the modulus of the base and the subgrade in the order of 0.319. Variability in Modulus of Unbound Layers

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It was initially assumed that the resilient modulus of the HMA layers follows a normal distribution. The validity of the previous assumption is now evaluated. LTPP contains HMA modulus for all flexible SPS sections and for many of the GPS sections. Information from 1137 HMA layers was identified Variability in Modulus of HMA Layers

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The data follows a uniform distribution. The CoV for the for the analyzed HMA layers were found to be on average 0.028 (0.001 – 1.645). Variability in Modulus of HMA Layers

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Three of the original SHRP climatic were selected: –Cold climatic region (Salem, OR), –Warm climate region (Destin, FL), and –Hot climatic region (Imperial, CA) A three-layer structure was analyzed Two types of truck traffic distribution (TTC2, TTC12) Material / Structural properties: Effect of Variability on MEPDG Predictions

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Simulation with the MEPDG was performed considering the previously defined design variables as random. Because the MEPDG has no closed-form solution Response surface approach. Fit a surface to the MEPDG predictions that can be later used to predict the performance 1,000,000 repetitions for each of the design scenarios were simulated. The effect of variability of the design parameters on different types of deterioration was assessed: –Rutting of the HMA layer, fatigue cracking, and IRI. MEPDG Performance Predictions

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Rutting –CV is on average 0.11 for the analyzed scenarios. –Ranged from 90% below the mean to 87% above the mean due to the variability of the design parameters. IRI –CV was con average 0.37 for the analyzed scenarios. –IRI in some of the cases was up to 581% above the mean. Fatigue Cracking –CV was con average 0.37 for the analyzed scenarios. –Ranged from 100% below the mean to 211% above the mean MEPDG Performance Predictions

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Most design and analysis tools assume that the input parameters are deterministic It has been shown that this assumption is unrealistic. When analyzing the variability and distributions of design variables, it was identified that some of the variables have considerable variation: –Layer thickness and resilient modulus of different layers. It is strongly advised that the analysis or design of the pavement structure be not only performed based on the mean design values, but at several other critical values of the variables that are expected to have a higher impact on the performance of the pavement structure. Conclusions

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Based on the different scenarios: For rutting and IRI Variability was higher on thin pavements in cool climatic regions or on thick pavements in warm climatic For fatigue cracking, Variability was more severe on all pavement structures under cool climatic regions. Conclusions

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Alguna Pregunta?

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Improving the Flexible Pavement IRI Predictions by Correcting for Possible Bias José P. Aguiar-Moya Harold von Quintus Dr. Jorge A. Prozzi

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Background –M-E IRI Model IV Regression –Panel Data Models Dataset for Model Estimation IRI Estimation Model Results Conclusions Presentation Outline

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Concept of Serviceability –Related to pavement performance → PSR & PSI Serviceability is correlated to IRI Background

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IRI measurement has improved –Highway speeds (profiler) Empirical Models to directly predict IRI –M-E PDG –Initial, Distress, Frost-heave, Swelling Background

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IRI Prediction Model M-E IRI Model

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Potential Problems: –Extrapolation of IRI to time of construction –Interpolation to match cracking/rutting observations –IRI estimated based on regression results Initial IRI should be captured thru intercept of model –Removes need for extrapolation Methods to account for correlation between regressors and unobserved factors M-E IRI Model

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OLS OLS Assumptions –E(X' ) = 0 (exogeneity) –Nonautocorrelation (uncorrelated errors) OLS Regression (M-E PDG)

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The Total is correlated with the regressors!! → Exogeneity assumption is not met → Biased estimates OLS Regression (M-E PDG)

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IV Regression Where = [ ], X i ' = [1,SF i,FC Total i,TC i,RD i ] Z i ' = exogenous variables IV Regression

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IV Regression by means of 2SLS –Project Z i on X i –Run least squares using projection of X i → COV[Z i, i ] = 0 → Estimates theoretically are consistent and unbiased IV Regression

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Data used for calibrating the IRI models contains –Cross-sectional observations –Time series observations Panel Data –Use time history of a pavement section as IV –Account for heterogeneity –Can use random-effects or fixed-effects approach Panel Data Models

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Fixed-Effects Random-Effects Panel Data Models

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Joint SF-IRI Fixed-Effects Panel Data Models

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Dataset for Model Estimation

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Instrumental Variables –Plasticity Index (PI) of the subgrade –Average annual precipitation in in. (Precip) –Frost Index (FI) –Age of the pavement in years (Age) –Gradation of the subgrade: material passing the 0.02 and 0.075 mm sieves (p02 and p075) –Thickness of the asphalt layer (h AC ) –Thickness of the granular base (h GB ) –Air voids (V a ) –Asphalt binder content (P b ) Dataset for Model Estimation

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(*) Using the 10 instrumental variables: PI, Precip, FI, Age, p075, p02, h AC, h GB, V a, and P b IRI Model Estimation Results

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Difference in estimates from OLS and IV Regression → Endogeneity Bias S.E. for the panel data model increased → Unobserved section specific attributes Panel Data Model parameters are more significant (by means of F-stat) LM test (H 0 : 2 u = 0) to test validity of pooled data models indicates there is bias due to unobserved variables Conclusions

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A Hausman test indicated that the assumptions of the R-E Model are inappropriate The F-E and the joint SF-IRI F-E Models are preferred Observed changes (OLS vs. F-E): –an increase of 1 ft in the length of transverse cracks has increased IRI by 38% –an increase of 1 ft 2 in the area of fatigue cracking has decreased IRI by 15% –an increase in the rut depth of 0.1 in. is associated with a 25% decrease in IRI Conclusions

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La Guia MEPDG esta aqui para quedarse Es el sistema de analysis de pavimentos mas completo hasta hoy Muy importante valor academico Representa “state-of-practise” Necesita muchas mejoras: –Calibracion a condiciones locales –Revision de modelos –Nuevos modelos –Simplifiacion de datos de entrada Una buena base de datos es esencial Final Conclusions

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