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Dr. Jorge A. Prozzi The University of Texas at Austin Valparaiso, Chile, 10 November 2010

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. Presentation Outline. Local Calibration of the Permanent Deformation Performance Models

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Dr. Jorge A. Prozzi The University of Texas at Austin Valparaiso, Chile, 10 November 2010

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  1. 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

  2. Presentation Outline • 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 ItsEffects • Improving the Roughness (IRI) Predictions by Correcting for Possible Bias

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

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

  5. 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.)

  6. Historical Background

  7. Historical Background • 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

  8. 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

  9. 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

  10. 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)

  11. 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

  12. 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

  13. Analysis Inputs - General

  14. LTPP SPS-5 sections

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

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

  17. 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

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

  19. Analysis Inputs – Material Gradation for both asphalt and unbound layers were also available Atterberg’s limits, MDD and OMC was available for unbound layers

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

  21. 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

  22. AC Rutting Transfer Function Hac = Total AC thickness (inches) εp = Plastic Strain (in/in) εr = Resilient Strain (in/in) T = Layer Temperature N = Number of Load Repetitions kz, k2, k3 = Laboratory Constants βr1, βr2, βr3 = Calibration Coefficients

  23. Methodology • βr1 is a shift factor • Governs the initial rut depth • βr3 accounts for the bias due to the number of load repetitions • Slope of the transfer function • βr2 is the bias correction factor for temperature susceptibility of hot mix asphalt • Not calibrated due to unavailability of data

  24. Level 2 Bias Correction Factors

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

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

  27. 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

  28. AlgunaPregunta?

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

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

  31. 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

  32. Traffic Input in M-E Guide

  33. 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

  34. Additive decomposition model Trend component Seasonal component Seasonal Time Series Model

  35. Seasonal Time Series Model • Linear growth plus seasonality • Compound growth plus seasonality

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

  37. 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

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

  39. Model Estimation Results

  40. Observed Vs. Predicted Traffic (2)

  41. Observed Vs. Predicted Traffic (1)

  42. Further Implication • Integrating long- and short- term traffic information

  43. 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

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

  45. AlgunaPregunta?

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

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

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

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

  50. WIM classification • Based on sensor technology • Load cell • Bending plate • Piezo-electronic Accuracy Cost

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