Toward Measuring Pavement Conditions to Optimize Roadway Treatments

Transcription

1 Toward Measuring Pavement Conditions to Optimize Roadway Treatments Kevin Carstens, EIT Robert Bertini, Ph.D, PE Anurag Pande, Ph.D Department of Civil and Environmental Engineering, Cal Poly San Luis Obispo Western District Annual Meeting 2015 Planet Hollywood Resort, Las Vegas, Nevada July 21 st, 2015 Source: calpoly.edu Source: lvite2015.com Background source: Kevin Carstens

2 Overview Introduction Data Acquisition Methodology Procedure Data Analysis Conclusions Source: Kevin Carstens Background source: en.wikipedia.org

3 Introduction Pavements typically biggest local agency asset Optimizing treatments is key issue This presentation will cover: Methodology of assessing pavement Analysis of results against independent variables Conclusions drawn from this analysis

4 Introduction San Luis Obispo y maintains: 1092 miles of paved road 17 signalized intersections 151 cattle guards Source: en.wikipedia.org Source: Kevin Carstens

5 Data Acquisition - Methodology MicroPaver Field Inspector Survey 1/10 road area Sample size: 2500 sq. ft. 25 wide road = samples 100 long every wide road = samples 50 long every 500 Segments Break road into user-friendly pieces Divided by major roads or mile-markers Cycle: every 3 years

6 Data Acquisition - Methodology Type of Distresses: Alligator cracks Longitudinal/traverse cracks Potholes Weathering Patching Edge cracking Etc. (21 total) Severity of Distresses: Low, medium, high Source: coastalroadrepair.com Source: ASTM D

7 Data Acquisition - Methodology Enter values for each stress into MicroPaver Program uses ASTM D to output PCI After completing all samples in a segment, program returns segment PCI Segment PCI can be compared against previous years

8 Data Acquisition - Procedure Calibrate survey vehicle using known mileposts Survey vehicle = super measuring wheel Used to locate samples within a segment (e.g. at 1000 ft., 2000 ft., etc.) Source: Kevin Carstens

9 Data Acquisition - Procedure One surveyor measured width Other measured length, using 2500 sq. ft. area Sprayed arrows to denote ends of sample Source: Kevin Carstens

10 Data Acquisition - Procedure Distresses split by surveyors Measuring wheels could record lengths and areas (and totals) Data then entered into MicroPaver and moved on to next sample Source: Kevin Carstens

11 Data Acquisition - Procedure Two teams Exchange progress daily Due to y size, 10/4 schedule (10 hours a day, 4 days a week) Source: Kevin Carstens

12 Data Analysis 2013 data 2007 data (two cycles before) y pavement treatment data Narrowed to Isolated possible independent variables: Climate Urbanization Traffic Volume Topography

13 Data Analysis Examples of differences climate: Cambria California Valley Source: Kevin Carstens Source: Kevin Carstens

14 Data Analysis Examples of differences climate: Source: SLO y EnergyWise Plan

15 Data Analysis Examples of differences urbanization: Arroyo Grande Hills Avila Beach Source: Kevin Carstens Source: Kevin Carstens

16 Data Analysis Examples of differences traffic volume: Cholame Nipomo Source: Kevin Carstens Source: Google Street View

17 Data Analysis Examples of differences topography: Soda Lake Source: Kevin Carstens Cambria Hills Source: Kevin Carstens

18 Data Analysis Notes: Red denotes cities Los Osos discounted from study Sources: Google Maps, Kevin Carstens

19 Data Analysis Data analyzed by zone Hypothesis was that: More urban -> higher traffic -> more damage More inland -> higher temperature swings -> more damage Turns out false

20 Data Analysis Analysis Zone Climate Zone Lake Nacimiento 4 R Paso Robles 4 R San Miguel 4 R Templeton 4 U Cambria 5 U Coastal 5 R Cayucos 5 U Edna/Los Osos Valleys 5 R Pozo 4 R Oceano 5 U Arroyo Grande and Nipomo Hills 5 R Nipomo Mesa 5 R Nipomo 5 U East y 4 R Weighted Climate 4 Weighted Climate 5 Weighted Rural Weighted Urban Weighted Total Rural or Urban None Chip Seal 1/4" Chip Seal 5/16" Pavement Treatment Overlay Thin Overlay 1" Overlay 2" Overlay 4" Reconstruct Structure

21 Data Analysis No pavement treatment: Weighted Climate 4 Weighted Climate 5 Weighted Rural Weighted Urban Weighted Total

22 Data Analysis Bottom line: Pavement Treatment None Chip Seal 1/4" Chip Seal 5/16" Overlay Thin Overlay 1" Overlay 2" Overlay 4" Reconstruct

23 Data Analysis Reduced data: Pavement Treatment None Chip Seal 1/4" Overlay 2" Overlay 4" Reconstruct

24 Data Analysis Further reduced data: Pavement Treatment None Chip Seal 1/4" Overlay 2" Overlay 4"

25 Conclusions Pavement engineer can apply values to generate a cost-benefit analysis local to their agency More expensive yields better results, where to draw the line? Climate nor urbanization affect degradation Most likely due to pavement design Further analysis should include: More data for sparse treatment categories (e.g. 5/16 chip seal) Wider band of analysis years, ideally whole pavement life cycles

26 Acknowledgements y of San Luis Obispo, Public Works Department Don Spagnolo, Transportation Programs Manager Cal Poly Department of Civil and Environmental Engineering Dr. Bertini and Dr. Pande Student Fee Initiative (SFI) Western District ITE

27 References SLO y PWD, Traffic and Transportation. [Online]. Available: [Accessed: 25-June-2015]. PG&E, California Climate Zone 4. [Online]. Available: lbox/arch/climate/california_climate_zone_04.pdf. [Accessed: 22-June- 2015]. PG&E, California Climate Zone 5. [Online]. Available: lbox/arch/climate/california_climate_zone_05.pdf. [Accessed: 22-June- 2015].

28 Questions? Background source: Kevin Carstens