Rideability Prediction of HMA Overlay Treatment of Flexible and Composite Pavements

Transcription

1 Rideability Prediction of HMA Overlay Treatment of Flexible and Composite Pavements By Muhammad Jamal Khattak Associate Professor University of Louisiana at Lafayette Muhammad A. Nur and Muhammad R. Bhuyan Graduate Research Assistant University of Louisiana at Lafayette

2 Presentation Layout 1. Background Pavement Treatment Study: LTRC-10-4P Research Objectives Research Phases 2. Development of IRI Models Data Source Project Selection Factors Effecting IRI Statistical Analysis IRI Model Behavior 3. Conclusions and Recommendations

3 Pavement Treatment Study LTRC-10-4P Background LADOTD has spent substantial financial resources on various rehabilitation/maintenance treatments to minimize the pavement distresses and improve the pavement life Effectiveness of any treatment largely depends on the time of treatment and trigger governed by treatment performance models. Recent study completed by LTRC regarding PMS emphasized the importance of developing treatment performance models

4 Pavement Treatment Study LTRC-10-4P Objective of Study Develop pavement treatment performance models in support of cost-effective selection of pavement treatment type and time of treatment.

5 Pavement Treatment Study LTRC-10-4P Three Phase Study 1. Phase I - Review and Project Selection 2. Phase II - Treatment Performance Modeling - Costs Benefits of Treatments 3. Phase III -Model Integration and Training

6 Development of International Roughness Index (IRI) Model

7 IRI Model Threshold RSL-1 IRI RSL-2 Elapsed Time (Year)

8 IRI Model Threshold RSL(BT) LE IRI RSL(AT) LE= RSL (AT) RSL (BT) Elapsed Time (Year)

9 Development of IRI Model Data Source Project Selection Factors Effecting IRI Statistical Analysis IRI Model Behavior

10 Data Source Historical Data LADOTD s mainframe database Material testing system (MATT) Tracking of Projects (TOPS) Letting of projects (LETS) Highway NEEDS, the traffic volumes data, and the pavement design and system preservation database.

11 Data Source Distress Data Distress data from PMS database IRI, Rut, Fatigue, Longitudinal and Transverse crackings Recorded every two years by the automatic road analyzer (ARAN) for every 1/10 th of a mile ( )

12 Data Source Climatic Data 20 weather stations throughout Louisiana-National Climatic Data Center (NCDC) Daily maximum, minimum and mean temperature ( ) Daily precipitation values from year ( ) Inverse distance weighting (IDW) method- For data interpolation.

13 Project Selection Project Selection All pavement sections having: One IRI data point just before treatment Three or more IRI data points after treatments Acceptance Criteria Criteria 1- One IRI data point before treatment. Criteria 2- Positive gain in distress based on the best-fit curve.

14 Project Selection Composite Pavement 78 projects were selected comprising of km (280 mile) Data averaged for each projects No. Observation=280 Flexible Criteria 170 projects were selected comprising of km (726.2 mile) Data averaged for each projects No. Observation=623

15 Factor Effecting IRI Factors Effecting IRI Equivalent Single Axle Load (ESAL) HMA Overlay thickness (T h ) PCC layer thickness (T PCC ) Functional classification (FC) IRI just before treatment (IRI p ) Temperature Index (TI) New Precipitation Index (PI)

16 Factor Effecting IRI Factors Effecting IRI New Temperature Index (TI) similar to Freezing Index (FI) is introduced to evaluate the effect of temperature. TI represents the variation of temperature of a particular project over the year with reference to 20 o C (68 o F) A negative One-degree day---> One day below 20 o C ---> {20-21=1 o C}. 1-day= 1- o C-day

17 Cumulative Degree-days Temperature Index (degree-days) Factor Effecting IRI Control Section TI for year Dec. 31 Jan. 31 Feb. 28 Mar. 31 Cumulative Degree-days Apr. 30 May. 31 Jun. 30 Jul. 31 Aug. 31 Sep. 30 Oct. 31 Nov. 30

18 Temperature Index ( C Days) Factor Effecting IRI 1400 Temperature Index (TI) Year LADOTD Districts

19 Cumulative Temperature Index ( C Days) Factor Effecting IRI Data Collection Year

20 Factor Effecting IRI Precipitation Index (PI) The PI is the product of cumulative precipitation/year and number of days/year of precipitation. PI= P. N p P is the total precipitation/year (cm), and Np is the number of days of precipitation in the year. PI represents the amount and exposure of pavement to moisture

21 Precipitation Index (cm-days) Factor Effecting IRI Precipitation Index (PI) For Year LADOTD Districts

22 Cumulative Precipitation Index (cm-days) Factor Effecting IRI Data CollectionYear

23 Regression Analysis HMA on Composite Pavement ln( CESAL) ln( IRI) ao a1.ln( IRI p ) a2.. FC a3. CTI. t a4. PI ( T / T ) h PCC Ln( SD o ) 0.362ln( IRI PP ) CESAL= Cummulative Equivalent Single Axle Load T h = HMA Overlay thickness T PCC = PCC layer thickness FC= Functional classification IRI p = IRI just before treatment CTI= Cummulative Temperature Index PI= Precipitation Index SD o = Standard Deviation t= Elapsed time after treatment

24 Regression Analysis Coefficients Regression Statistics Multiple R 0.79 R Square 0.63 Adjusted R Square 0.62 Standard Error Observations 280 F-statistics Significance-F 1.61x10-56 Value Standard Error t-stats p-values a o x10-21 a a x10-11 a x x a x x

25 Predicted Ln(IRI), (cm/km) Regression Analysis 7 6 n=280 R 2 = Actual Ln(IRI), (cm/km)

26 Residual Regression Analysis Predicted Ln(IRI), (cm/km)

27 Regression Analysis HMA on Flexible Pavement 1 ln( CESAL ) ln( IRI ) ao a1.( ) a2. a3. TI a4. CPI. t FC ( Th ) Ln( SD ) 0.513Ln( IRI ) 1 o PP CESAL= Cummulative Equivalent Single Axle Load T h = HMA Overlay thickness FC= Functional classification IRI pp = Existing/Current IRI value TI= Temperature Index PI= Cummulative Precipitation Index SD o = Standard Deviation t= Elapsed time after treatment

28 Regression Analysis Regression Statistics Multiple R R Square 0.47 Adjusted R Square 0.46 Standard Error 0.16 Observations 623 F-statistics Significance-F 2.17x10-82 Coefficients Value Standard Error t-stats p-values a o a x10-06 a x10-16 a x x x10-05 a x x x10-10 a x10-49

29 Predicted Ln(IRI), (cm/km) Regression Analysis 6 n=623 R 2 = Actual Ln(IRI), (cm/km)

30 Residual Regression Analysis Predicted Ln(IRI), (cm/km)

31 Normalized Frequency Cumulative % Regression Analysis % of Data used for Modelling 25 % of Data used for Predicting 100% 90% 80% 70% % 50% % 30% % 10% Error (%) 0%

32 IRI, (cm/km) Model Behavior Composite Time, (Years) Threshold

33 IRI, (cm/km) Model Behavior Flexible Time, (Years) Threshold

34 Conclusions IRI largely affected by cumulative ESAL, thickness of the pavement, temperature and precipitation. IRI of the overlay was a function of the pretreatment condition of the road and highway functional classification. IRI prediction models were developed which exhibited good agreements between the measured and predicted IRI values. Newly developed temperature index (TI) and precipitation index (PI) showed strong statistical significance for predicting IRI. It is postulated that the developed models will be a good pavement management tool for predicting the IRI of the overlay treatment, which will facilitate timely maintenance and rehabilitation action.

35 Thanks!