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JCP Performance Prediction Models for PMIS: Overview, Results, and Recalibration Objectives

This presentation outlines the methodologies and findings of proposed performance prediction models for Jointed Concrete Pavements (JCP) within the Pavement Management Information System (PMIS). Key topics include an overview of JCP distresses, original models' objectives, recalibration efforts, and detailed results addressing failures, patches, longitudinal cracks, and shattered slabs. Estimated ages and treatments, as well as significant modeling factors, are discussed to optimize maintenance strategies. The analysis leverages a robust dataset of over 30,000 records, focusing on improving predictive accuracy and maintenance interventions.

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JCP Performance Prediction Models for PMIS: Overview, Results, and Recalibration Objectives

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  1. Implementation Project 5-6386-1 Proposed JCP Performance Prediction Models for PMIS

  2. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  3. JCP Distresses in PMIS Overview

  4. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Methodology • Estimated age • Estimated treatments • Modeling groups • Recalibration

  5. Original Models σ=Subgrade factor=1 α,β,ρ control curve shape χ=Traffic factor ε=Climatic factor

  6. Recalibration Objectives For 5 JCP distresses and ride score, find {α,β,ρ} for: 2 JCP types 2 3 3 climatic zones 1 2 3 3 traffic levels Low Medium Heavy 4 treatments: PM=preventive maintenance LR=light rehabilitation MR=medium rehabilitation HR=heavy rehabilitation PM LR MR HR TOTAL: 72 combinations

  7. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  8. Available Data 30,831 records (1993-2010) Real age available for 2,750 records Dallas: 11,578 Houston: 10,754 Childress: 713 Beaumont: 7,786 Estimated age Classified into categories and utilized: 29,627 records

  9. Data Treatment Objectives • Minimize the influence of JCP distresses decreasing due to: • Maintenance policies and/or • Distress progression • Estimate JCP age (not available in PMIS) • Estimate JCP treatments (not available in PMIS) • Determine significant modeling factors, grouping where applicable

  10. Failure Failed Js&Cs JCP Distress Progression Concrete patch other cracks Shatteredslab Longitudinal crack

  11. Effect of Maintenance and Distress Progression JCP Distress Age

  12. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  13. Estimated Age Best estimate based on 2,750 real ages R2=56%, all coefficients’ P-values < 0.0001

  14. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  15. Estimated Treatments

  16. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  17. Significant Modeling Factors 2 JCP types non-significant 2 3 3 climatic zones non-significant for HR 1 2 3 3 traffic levels Low Medium Heavy 4 treatments: PM=preventive maintenance LR=light rehabilitation MR=medium rehabilitation HR=heavy rehabilitation PM LR MR HR TOTAL: 20 significant combinations

  18. 20 Modeling Groups JCP 2=3

  19. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  20. For Each Distress Type: • Test statistical significance of modeling factors, group when non-significant • Examine the data for adherence to the logical order of performance: HR > LR > PM Low traffic > medium traffic > heavy traffic • Examine statistical summaries and bubble plots of the data to determine seed values and boundaries for the model coefficients • Fit the HR model for the traffic level that best adheres to the data • Constrain the remaining HR model coefficients, fit the models and calculate the percent RMSE change with respect to the original model • Constrain the LR model coefficients, repeat steps 1 to 5 • Repeat for PM Procedure: SAS proc nlin. If no convergence, fit based on RMSE reduction.

  21. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  22. Failed Joints and Cracks

  23. Failed Joints and Cracks Zone 1

  24. Failed Joints and Cracks Zones 2 & 3

  25. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  26. Failures

  27. Failures Zone 1

  28. Failures Zone 2

  29. Failures Zone 3

  30. Presentation Outline • Results • Failed joints and cracks • Failures • Concrete Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  31. Concrete Patches

  32. Concrete Patches Zone 1

  33. Concrete Patches Zone 2

  34. Concrete Patches Zone 3

  35. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  36. Longitudinal Cracks

  37. Longitudinal Cracks Zone 1

  38. Longitudinal Cracks Zone 2

  39. Longitudinal Cracks Zone 3

  40. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  41. Shattered Slabs

  42. Shattered Slabs Shattered slabs=0 for approximately 98% of the data

  43. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  44. Ride Score • Slab warping = JCP roughness (FHWA, 2010) • Roughness = JCP condition (Lukefahr, 2010) Therefore: • JCP ride score =f(random moist/temp gradients) % Data Ride score

  45. Ride Score Conclusion: best prediction is last year’s measurement

  46. Presentation Outline • Results • Failed joints and cracks • Failures • Patches • Longitudinal cracks • Shattered slabs • Ride score • Conclusions • Overview of JCP distresses in PMIS • Original models and recalibration objectives • Available data • Methodology • Data treatment • Estimated age • Estimated treatments • Modeling groups • Recalibration

  47. Conclusions • 66 new JCP distress models • 1 model = original • 59 models—RMSE decreased (improvement) • 6 models—RMSE increased (constrained models based on engineering judgment) • Average RMSE change = +27.72% • Ride score’s best prediction is previous year measurement

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