Prediction of Thermal Behavior of Pervious Concrete Pavements in Winter

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1 Prediction of Thermal Behavior of Pervious Concrete Pavements in Winter Somayeh Nassiri, PhD, PEng Assistant Professor, Civil & Environmental Engineering Department Washington State University 1

2 Research Impetus Increased use of pervious concrete pavements in cold climate areas Maintaining winter safety/mobility require proper winter maintenance Timely salting, anti-icing, deicing Research needs 1) PCP thermal properties & behavior 2) PCP temperature prediction as groundwork for winter maintenance practices 2

3 Project Objectives 1. Experimentally determine thermal conductivity (k) of pervious concrete in variable porosity 2. Predict PCP surface temp in winter based on field data 3. Develop relationship to predict surface temperature based on ambient conditions 3

4 Laboratory Experiment Two methods to determine k: ASTM C Heat flow meter (FOX 304 by LaserComp TA instruments ) ASTM D Heat impulse (Decagon RK-1 Rock Sensor Kit) 4

5 Mixture Design Mixture design Constituents in SSD conditions lb/yd 3 Coarse Aggregate 2,635 Type I/II Cement 568 Class F Fly ash 100 Water 161 Specimens 20 Cylinders (φ4 8 ) cut in half 6 Slabs (~ ) 5

6 Specimens Casting & Curing Compaction Slabs porosity: 19, 21, 24, 26, 31, 36% Cylinders: 16-23% Curing Demolded & tested after 7 days air cured in lab Compaction using Proctor hammer 6

7 Physical Properties Hardened Porosity (ASTM C1754) AAAAAA VVVVVVVV CCCCCCCCCCCCCC = (1 MM ww MM dd ρρ ww VV ) Infiltration Test (ASTM C1701) M: mass of infiltrated water in lbs, D: inside diameter of infiltration ring in inch T: time required for designated mass of water to infiltrate through PC in seconds K: correction factor equal to 126,870 inch. 7

8 Thermal Conductivity 1. Heat flow meter (ASTM C518) Conductivity based on Fourier s Law: ability of material to transfer heat through a unit-length thickness. qq = KK FOX 304 Heatflow meter 8

9 Thermal Conductivity 2. Sensor probe RK-1 Rock Sensor Kit Applies heat impulse Auto log data 9

10 Test Results-Slabs Heatflow meter Sensor probe Measured K, W/(mK) k probe = -1.25φ R² = k heatflow = -1.52φ R² = % 20% 25% 30% 35% 40% Porosity 1 W/(mK) = Btu/(hr ft F) RK-1 higher k than FOX 304 FOX 304 shows less variation Due to temp-controlled environment 10

11 Comparison of Two Methods Heat Flow Meter captures k of slabs using whole depth from top to bottom in several heat steps. Values of k from RK-1 Sensor based on contact area of probe & specimen. Probe length 2.33 in bottom 1.67 in of slab neglected. Heatflow K Convert RK2 to heatflow y = x R² = Sensor K 11

12 Test Results-Cylinders K = -0.66φ R² = 0.23 K (W/mK) K = -1.41φ R² = Bottom Half Top half % 15% 20% 25% 30% Top or Bottom Half Porosity Bottom halves: k = 0.49 W/(mK) Top halves: k = 0.56 W/(mK) for top halves 12

13 Geometric Parallel Model llllllll pp = VV aaaaaa llllllll aaaaaa + VV pppppppppp llllllll pppppppppp + VV aaaaaa llllllll aaaaaa Air voids, V air, k air Q (Nassiri & Nantasai 2017) Cement Paste, V paste, k paste Coarse Aggregate, V agg, k agg k agg (crushed Basalt) K paste K air W/(mK) W/(mK) W/(mK) V air = φ V agg V paste

14 Model s Accuracy Slabs Predicted K (W/mK) Dry slabs y = 1.016x R² = :1 line Measured K (W/mK) 14

15 Model Predictions Cylinders 0.80 Predicted K (W/mK) y = 0.72x R² = 0.49 Bottom Half 0.40 y = 0.66x R² = 0.21 Top Half :1 Line Measured K (W/mK) 15

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17 Instrumented Field Project Weather station on campus Ambient temp, RH, wind speed Solar radiation from on-site pyranometer Solar sensor Pyranometer Datalogger sensor trees Pervious concrete sidewalk 17

18 Sensor Tree Details Two sensor trees: Monitor volumetric water content & temp of PCP (5TE sensors) Seven thermocouples 18

19 Ambient Temp & Solar Radiation No. of days that temp fell below 32F: Year 1: 39 days Year 2: 64 days 19

20 Pavement Temp Data Sensor Tree A and B same data No. of days that temp fell below 32F: Year 1: 43 days Year 2: 66 days 20

21 Pavement Temp Data 21

22 Boundary Conditions Predict surface temperature for winter maintenance Enhanced Integrated Climatic Model (EICM) dd dddd dddd KK dddd = ρρ CC pp dddd dddd (Dempsey et al. 1983) QQ rrrrrr = QQ ss QQ rr QQ ee + QQ aa Q s = Radiation from space, Btu/hr ft 2 F, Q r = Reflection from clouds, Btu/hr ft 2 F, Q e = Reflection from the pavement, Btu/hr ft 2 F, Q a = Radiation bounced from clouds. Btu/hr ft 2 F. 22

23 In-situ Porosity Total Porosity & Infiltration PP = P = whole porosity, % I = Infiltration (cm/hr) (Haselbach & Freeman 2006) Porosity of each Layer PP tttttt =1.07P-7 PP mmmmmm =P PP bbbbbb =0.93P+7 P top = Porosity 1/3 PCP depth, % P mid = Porosity 2/3 PCP depth, % P bot = Porosity 3/3 PCP depth, % (Haselbach et al. 2016) 23

24 Estimate Thermal Properties Thermal conductivity: llllllll pp = nn aaaaaa llllllll aaaaaa + nn pppppppppp llllllll pppppppppp + nn aaaaaa llllllll aaaaaa Specific heat (Cp) of PC from Cp of components CC pp = xx aaaaaa CC aaaaaa + xx cccccccc. CC cccccccc. + xx aaaaaa CC aaaaaa x agg, x conc & x air : volumetric fractions C agg, C conc & C air : specific heat of aggregate, cement paste, & air Constituent Volume Fraction (%) Cp Btu/(lb F) K BTU/(ft 2 hr F) Limestone Coarse Aggregate Cement Paste Air References available in Nantasai (2016) 24

25 EICM Prediction Discussion Overall good agreement Past Day 100 : highly influenced by solar radiation & require characterization of surface emissivity 0.5-inch depth 25

26 Predicted Vs. Measured Temp Best agreements achieved when ambient temp between F 80 Predicted Temp, F y = 0.82x R² = Measured Temp, F Temp at 0.5 inch 1:1 line 26

27 Regression Model TTsurf = TT aaaaaa h cc SS PP RRRR TT aaaaaa h 2 cc TT aaaaaa h cc TT aaaaaa SS TT aaaaaa RRRR h cc SS SS RRRR PP RRRR T amb = Ambient temperature, F P = Precipitation, in RH = Relative Humidity, %. h c = Wind speed, mph S = Percent sunshine, % Identify freezing potential of surface out of 121 days: 67 days measured Vs. 57 days predicted 27

28 Conclusions Thermal conductivity of slabs obtained using heat flow Experiment expanded to cylinders using needle probe Relationship developed between k and porosity Linear relationship developed to predict thermal properties based on mixture design Best estimate of in-situ porosity required 28

29 Conclusions In-field temp measurements form instrumented section used to validate a temp model EICM surface temp prediction agreed with 85% of measured temp Linear regression model developed using major climatic indices to estimate surface temp with good agreement 29

30 Recommendations More mixtures designs, structural designs need tested to expand parallel model Mixtures with a variety of aggregate to be tested Expand modeling to non-overcast conditions Surface emissivity Evaporation at surface Presence of water 30

31 Acknowledgements Center for Environmentally Sustainable Transportation in Cold Climates (CESTiCC) for funding Washington State Facilities for in-kind support and collaboration during pavement installation & instrumentation Benjamin Nantasai, MS student that worked on the project at the time Dr. Liv Haselbach for collaboration 31

32 Ongoing Research Skid Resistance in dry, wet, ice Application of anti/deicer agents 32

33 References Nassiri, S. & B. Nantasai. (2017). Thermal Conductivity of Pervious Concrete for Various Porosities, ACI Materials Journal, 114 (2). Dempsey, B.J., Herlache, W.A., & Patel, A.J. (1983). Environmental Effects on Pavements Theory Manual, Volume 3 (Report No. FHWA/RD-84/115), Federal Highway Administration, McLean, Virginia. Nantasai, B. (2016). Temperature Predictions in Pervious Concrete Pavement During Cold Months (Master thesis, Washington State University). Haselbach, L. M., & Freeman, R. M. (2006). Vertical porosity distributions in pervious concrete pavement. ACI materials journal, 103(6), Haselbach, L.M., Personal Communication, February

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