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Implementation Project 5-6386-1

Implementation Project 5-6386-1. Proposed JCP Performance Prediction Models for PMIS. Presentation Outline. Results Failed joints and cracks Failures Patches Longitudinal cracks Shattered slabs Ride score Conclusions. Overview of JCP distresses in PMIS

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Implementation Project 5-6386-1

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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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