1. Incorporating Physical and Chemical Characteristics of Fly Ash in Statistical Modeling of Binder Properties FINAL EXAM - MSCE Prasanth Tanikella Major Professor: Jan Olek Department of Civil Engineering 1 July 23 rd, 2009 Prasanth Tanikella - Purdue University

2. Objectives and Hypothesis The goal of this research was to: – Characterize two sets of fly ashes (Class C and Class F) – Statistically verify the importance of their physical and chemical properties on the performance of binary and ternary paste systems Scope of the Project (3 Phases) – Phase 1 – Characterization of Fly Ashes – Phase 2 – Effect of Fly Ashes on the Properties of Binary Paste Systems (cement + fly ash) – Phase 3 – Effect of Fly Ashes on the Properties on Ternary Paste Systems (cement + two different fly ashes) Prasanth Tanikella - Purdue University 2

3. Phase 1 – Characterization of Fly Ashes Collected 20 different fly ashes (13 Class C and 7 Class F) 15 of them ( 9 Class C ashes and 6 Class F ashes) are currently on the INDOT’s list of approved pozzolanic materials A database summarizing the physical and chemical characteristics of the collected fly ashes and the impact of these properties on the behavior of binders would benefit the engineers, contractors and concrete producers Test Methods Prasanth Tanikella - Purdue University3 Phase 1 Total Chemical Analysis and loss-on ignitionASTM C 311 Soluble Sulfates and AlkalisIon Chromatography Particle Size Distribution Laser Particle Size Analyzer and Sedimentation Analysis Magnetic ParticlesTeflon coated bar magnet Crystalling component and glass fractionX-ray Diffraction MorphologySEM Strength Activity IndexASTM C 311

4. Results Range of chemical compositions CLASS CaO (%) SiO 2 (%) Al 2 O 3 (%) Fe 2 O 3 (%) Sulfate (%) Alkali Content as Na 2 O (%) LOI(%) F 1 - 939 - 5618-295 - 250.4 - 21.4 – 2.61.4 – 2.4 C 17 -2832 -4417 - 226 - 100.05 – 1.31.6 - 3.90.25 - 0.9 * INDOT list of approved fly ashes Phase 1 4Prasanth Tanikella - Purdue University CLASS CaO (%) SiO 2 (%) Al 2 O 3 (%) Fe 2 O 3 (%) Sulfate (%) Alkali Content as Na 2 O (%) LOI(%) F1 - 939 - 5618-295 - 250.4 - 21.4 – 2.61.4 – 2.4 C17 -2832 -4417 - 226 - 100.05 – 1.31.6 - 3.90.25 - 0.9

5. Results XRD – Typical Class F Fly Ash  Typical X-ray patterns for Class F fly ashes  Includes 1. Quartz – SiO 2 2. Mullite – Al 6 Si 2 O 13 3. Anhydrite – CaSO 4 4. Hematite – Fe 2 O 3 5. Magnetite – Fe 3 O 4 6. Lime – CaO Measured magnetic content is generally very high (with two exceptions) A hump, representing a silica-type glass with a maximum at 2θ=~25° is visible Glass “hump” is generally higher than that observed for Class C ashes Prasanth Tanikella - Purdue University 5 XRD pattern for Elmer Smith fly ash Phase 1 XRD pattern for Miami 7 fly ash

6. Results XRD - Typical Class C Fly Ash  X-ray pattern for a typical Class C fly ash  Includes 1. Quartz – SiO 2 2. Anhydrite – CaSO 4 3. Merwinite – Ca 3 Mg(SiO 4 ) 2 4. Periclase – MgO 5. Lime – CaO Glass peak is similar for all the ashes of this type Magnetite might be present in the fly ash, either in crystalline form or in the glass A hump, representing a calcium- aluminate type of glass with a maximum at 2θ=~30° is visible Prasanth Tanikella - Purdue University6 XRD pattern for Hennepin fly ash Phase 1

7. Results XRD – Glass Content Estimation  Glass content was empirically estimated by calculating the area under the glass hump Three softwares were used for the purpose xyExtract – To extract points from the XRD pattern LabFit – To fit the curve very precisely through the extracted points Sicyon Calculator – To integrate the fitted curve Prasanth Tanikella - Purdue University7 Phase 1

8. Results Particle Size Distributions Class F and Class C ashes form two different bands of PSDs The band of Class C ashes is shifted towards the left of the band of Class F ashes Prasanth Tanikella - Purdue University8 Class C Class F Phase 1

9. Results Discrepancies in PSD  Discrepancies observed in PSD  The pipette analysis seems to work well for particles larger than 5 micron  The results below 5 microns seem to diverge from either of the curves  Even though the sedimentation technique does not work well for particles smaller than 5 microns, based on the data it is reasonable to assume that the PSD based on Lab 1 (Purdue) data is accurate Prasanth Tanikella - Purdue University9 Phase 1

10. Results Morphology of class F (Type I) ashes  There is a large variation in the sizes and shapes of the particles  Particles with rugged surface are generally magnetic, contrary to the class C fly ashes  Many hollow particles present  Relatively smaller number of unburnt carbon particles, but bigger particles have been observed, which is consistent with the higher LOIs values observed in Class F ashes Prasanth Tanikella - Purdue University 10 Mill CreekPetersburg Elmer SmithZimmer

11. Results Morphology of class C ashes  Wide range of sizes of spherical particles  Many hollow particles with shell generally composed of silica and alumina  Frequent irregularly-shaped particles (often with rugged surfaces) predominantly composed of sulfates or magnesium, or rarely sodium Prasanth Tanikella - Purdue University11 Rush Island KenoshaLabadie Will County

12. Summary – Phase 1 Characterization of fly ashes Significant variations in the chemical and physical characteristics of fly ashes observed The strength activity index of Class C ashes was higher than Class F ashes The glass content for all the Class C ashes was higher than the glass content for all but two Class F ashes, thus indicating that although Class C fly ashes have less glass than these two Class F ashes, the glass in Class C ashes is more reactive The morphology of the ashes was similar irrespective of the class, with a few exceptions The particle size distributions of class C and class F ashes were significantly different All mean particle sizes in class F were larger than mean particle sizes in class C ashes, resulting in a lower surface area of class F ashes The LOI values of all class F ashes were higher than that of the C ashes Prasanth Tanikella - Purdue University 12 Phase 1

13. Phase 2 - Evaluation of the hydration characteristics of cement-fly ash binder systems  Binder systems consisted of portland cement with 20% (by weight) replaced by fly ash  Pastes with constant water/binder ratio (0.41) were tested for various properties including,  Initial Time of Set – Vicat needle (ASTM C 191)  Heat of Hydration – Isothermal Calorimetry (at a constant temperature of 21 o C)  Amount of Calcium Hydroxide at ages 1, 3, 7 and 28 days - TGA  Non-evaporable water content at 1,3 7 and 28 days – TGA  Rate of strength gain at 1, 3, 7 and 28 days – Strength activity index (ASTM C 311) Prasanth Tanikella - Purdue University13

14. Initial Setting Time - Results  Range of set time for Class C ashes – (1 hour to 4.5 hours)  Range of set time for Class F ashes – ( 2.5 hours to 3.5 hours) Prasanth Tanikella - Purdue University14 Phase 2

15. A Typical Calorimeter Curve Data acquired from the calorimeter curve  Peak heat of hydration (W/kg)  Time of peak heat of hydration (minutes)  Total heat of hydration (J/kg) – ( Area under the curve from 60 minutes to 3 days) Prasanth Tanikella - Purdue University15 Phase 2 Total Heat Time of Peak Heat

16. Peak Heat of Hydration - Results  Most ashes tend to reduce the peak heat of hydration compared to cement  Class F ashes in general have a higher peak heat of hydration than Class C ashes  Kenosha, the fly ash with the lowest peak heat of hydration had a flash set Prasanth Tanikella - Purdue University16 Phase 2

17. Time of Peak Heat of Hydration - Results  Most ashes tend to delay the occurrence peak heat of hydration compared to cement  Class C ashes in general have a higher time of peak heat than Class C ashes  Kenosha, the fly ash with the lowest peak heat of hydration had longest time of peak heat Prasanth Tanikella - Purdue University17 Phase 2

18. Total Heat of Hydration - Results  Most ashes tend to reduce the total heat of hydration compared to cement  Most Class C ashes have a similar total heat of hydration  Quite a few of the Class F ashes have a similar total heat of hydration as that of most Class C ashes Prasanth Tanikella - Purdue University18 Phase 2

19. Thermo-gravimetric Analysis (TGA)  Calcium hydroxide content and non- evaporable water content were estimated using TGA at various ages (1, 3, 7 and 28 days)  Calcium Hydroxide content between 480 o C and 550 o C (carbonation taken in to account)  Non-evaporable water content calculated according to Barneyback, 1983. Prasanth Tanikella - Purdue University19 Phase 2

20. Calcium Hydroxide Content at 1 day - Results  Most ashes tend to reduce the amount of calcium hydroxide at 1 day compared to plain cement paste (with some exception)  Class F ashes have a slightly higher CH content than Class C ashes at early ages Prasanth Tanikella - Purdue University20 Phase 2

21. Calcium Hydroxide Content at 28 days - Results  Most of the ashes show a higher amount of calcium hydroxide at 28 day compared to plain cement paste  Difference in the rates of reactions in the fly ashes Prasanth Tanikella - Purdue University21 Phase 2

22. Non-evaporable Water Content at 1 day - Results  Most ashes tend to lower the amount of non-evaporable water content at 1 day compared to plain cement paste  Plain cement has a higher degree of hydration than most of the fly ash pastes Prasanth Tanikella - Purdue University22 Phase 2

23. Non-evaporable Water Content at 28 days - Results  Most of the Class C ashes show a higher amount of non-evaporable water at 28 day compared to Class F ashes  Difference in the rates of reactions in the fly ashes Prasanth Tanikella - Purdue University23 Phase 2

24. Strength Activity Index at 28 days - Results  All of the Class C ashes show a higher strength at 28 days compared to plain cement paste while Class F ashes show a lower strength comparatively Prasanth Tanikella - Purdue University24 Phase 2

25. Statistical Modeling of Binary Binders Phase 2 STEP 1 - Perform linear regression analysis for each of the 16 dependent variables (hydration related properties of ashes) using all the data points (13 Class C and 7 Class F binary pastes) STEP 2 - Prepare a table with a list of models containing the sets of independent variables that must affect the dependent variables, in a decreasing order of "Adj-R 2 " (only models with the best 10 adj-R 2 values were included) STEP 3 - Perform linear regression analysis for the same set of 16 dependent variables as in Step 1, but using only those independent variables that were selected based on Step 2 for both Class C and Class F ashes separately STEP 4 - If both the model for Class C and Class F ashes are statistically significant, the set of variables selected in Step 2 is used in the formulation of the experiments for the ternary paste systems Prasanth Tanikella - Purdue University25

26. Statistical Modeling of Binary Binders Phase 2 Independent VariablesAbbreviations Physical Properties Mean Particle Sizemeansize Specific surface area measured using Blaine's apparatusblaines Specific surface area measured using laser particle size analyzer Spsurface Chemical Properties Calcium oxide contentcao Sum of silicon, aluminum and iron oxide contentsSAF Magnesium oxide contentmgo Aluminum oxide contentAlumina Sulfate contentsulfate Physico-chemical Properties Loss-on ignitioncarbon Glass content measured using X-ray diffraction glass Prasanth Tanikella - Purdue University 26

27. Dependent Variables Prasanth Tanikella - Purdue University27

28. Ten Models with the highest Adj-R 2 – Set Time Phase 2 Model Number Number of Variables in the model Adjusted R 2 R2R2 Variables in the model 1 30.24470.3706sulfate, alumina, glass 2 50.22980.4437sulfate, SAF, mgo, alumina, glass 3 20.2230.3093sulfate, alumina 4 70.21890.5226 spsurface, meansize, sulfate, carbon, SAF, alumina, glass 5 10.2170.2605sulfate 6 70.20990.5172 spsurface, meansize, sulfate, carbon, cao, alumina, glass 7 60.20950.473 spsurface, sulfate, SAF, mgo, alumina, glass 8 40.20890.3847sulfate, SAF, mgo, alumina 9 20.20320.2917sulfate, carbon 10 50.20080.4228 spsurface, sulfate, SAF, mgo, alumina 28 Prasanth Tanikella - Purdue University

29. ANOVA Table (Class C Ashes) – (SAI) at 28 days Phase 2 Prasanth Tanikella - Purdue University SourceDF Sum of Squares Mean SquareF Valuep-Value Model3470.0203156.673434.4449770.0407 Error8281.978435.2472975 Total11751.9987 R2R2 0.625 Adj - R 2 0.4844 VariableDF Parameter Estimate Standard Errort-Valuep-Value Intercept1142.04341.6683785.139020.0036 meansize1-1.5730.10387-15.14390.0246 sulfate1-15.78470.01841-857.3980.0135 SAF10.04960.118430.4188130.9266 29

30. Observed Vs Predicted (Class C Ashes) – Set Time Phase 2 Prasanth Tanikella - Purdue University30

31. ANOVA Table (Class F Ashes) – (SAI) at 28 days Phase 2 Prasanth Tanikella - Purdue University SourceDFSum of SquaresMean SquareF Valuep-Value Model3107.6567635.8855940.130.0244 Error21.788390.894195 Total5109.44515 R2R2 0.9837 Adj - R 2 0.9591 VariableDF Parameter Estimate Standard Error t-Valuep-Value Intercept1126.1375817.789757.0904640.0193 meansize1-0.671930.23397-2.871860.1029 sulfate1-9.276741.16409-7.969090.0154 SAF1-0.003290.1415-0.023250.9835 31

32. Observed Vs Predicted (Class F Ashes) – Set Time Phase 2 Prasanth Tanikella - Purdue University32

33. Summary- Phase 2 Binary Binder Systems Phase 2 Prasanth Tanikella - Purdue University PropertyMost Influencing VariablesSignificant Variables Set TimeSulfate, alumina, glassNone Peak HeatSpsurface, SAF, glassSpsurface, CaO TimepeakSpsurface, Meansize, MgOSpsurface Total HeatMeansize, carbon, SAFMeansize Ca(OH) 2 Blaines, Spsurface, sulfate, cao, glass, carbon, alumina Blaines Wn Blaines, carbon, alumina, sulfate SAF, mgo Blaines SAI 7 DaySAF, CaO, GlassSAF, CaO SAI 28 DayMeansize, sulfate, SAFMeansize, Sulfate Physical characteristics of fly ash had a higher effect than chemical characteristics of fly ash Surface area was found to be the most influencing variable affecting most of the properties of the binder system at both early and later ages Variables including SAI (at later ages) and time of peak heat of hydration can be predicted accurately using the respective statistical models 33

34. Phase 3 – Ternary Binder Systems  Ternary Binder System – Cement + 2 different fly ashes  Total 20 % of the cement replaced with the mixture of fly ashes at specific percentages  Water/binder ratio was 0.41 (unless specified in the standard) OBJECTIVES 1.To ascertain the applicability of the weighted sum of the models chosen for the binary paste systems to predict the properties of ternary binder systems. 2.The analysis of which of the chosen independent variables (from binary binder models) have the highest effect on the properties of ternary systems Prasanth Tanikella - Purdue University34

35. Ternary Binder Systems Experimental Design  Full factorial design consists of 180 experiments when the ratio of the two fly ashes is fixed  Fractional factorial design – Orthogonal Array Technique (Taguchi Method) Requirements of a Fractional Factorial Design  Reduction in the number of experiments  The data should be a representative data set of the full factorial design  The quality of the inferences obtained should be similar to the inferences from the full factorial design Prasanth Tanikella - Purdue University35 Phase 3

36. Orthogonal Array Technique – Taguchi Method  A special test matrix is prepared  Columns – Factors (Dependent Variables)  Rows – Each row is an experiment (Mix design)  Values in the table – Factors levels, levels at which the three factors are varied Prasanth Tanikella - Purdue University36 Phase 3 Factors ExperimentABC 1111 2122 3132 4212 5223 6231 7313 8321 9332 Factors ExperimentABCD 11111 21222 31333 42123 52231 62312 73132 83213 93321

37. Test Matrix for Set Time  Columns – Factors (Sulfate, Alumina, Glass)  Rows – Each row is an experiment (Mix design)  Factor Levels – 33.33, 50 and 66.67 percentile of the available data set Prasanth Tanikella - Purdue University37 Phase 3 ExperimentGlassSulfateAlumina 11 (0.4347)1 (18.75)1 (1.294) 2122 3133 4212 5223 6231 7313 8321 9332 Levels  Factors123 Sulfate (%)0.43470.52810.7593 Alumina (%)18.7519.2820.07 Glass1.2941.4761.513

38. Scaled Standard Deviation - SSD It is practically not possible to choose two ashes (in any proportions) having a target combination of three different factors Standardizing the Error in the Fly Ash Combinations Scaled Standard Deviation (SSD) to evaluate the error of the combination Prasanth Tanikella - Purdue University38 Phase 3 SSD = SSD values up to 0.3 were found to give a good approximation of the target values

39. Analysis of the Data - Additivity Model 1 – The two models obtained for Class C and Class F ashes from the binary binder, with the chosen independent variables (factors) were used to predict the properties (dependent variables) for both the Classes of ashes separately. The two predicted values were then added in the proportions of the added fly ashes to obtain the final value of prediction for the ternary binder system. This value was compared with the experimentally observed values. Prasanth Tanikella - Purdue University39 Phase 3 Model 2 – The best models obtained for Class C, Class F ashes individually were used to predict the properties of the ashes in the mixture separately, and the predicted values of the properties were added in the proportion of the ashes to obtain the final value of the predicted properties of the ternary binder systems. Model 3 – The model obtained for the entire set of Class C and Class F ashes together using all the 20 data points, containing the best three chosen independent variables was used to predict the properties of Class C and Class F ashes separately.

40. Analysis(Objective 2) – Influencing Variables Prasanth Tanikella - Purdue University40 Phase 3 Analysis of Variance (ANOVA) – Factor level ANOVA Total Sum of Squares, S T = Variation caused by a single factor A, S A = - where, N A1 = total number of experiments in which level 1 of factor A is present A 1 = the sum of the results of level 1 of factor A (X i ) Mean squares (Variance): V A = Pure sum of squares: S A ’ = S A – (V e x f A ) Percent Influence: P A = where, f A is the degrees of freedom for factor A V e is the variance for the error term, which is calculated as S e = error sum of squares f e = error degrees of freedom T = sum of the results (X i ) and N is total number of results

41. Test for Additivity – SAI 28 days (%) Prasanth Tanikella - Purdue University41 Phase 3 Model 1Model 2Model 3 Exp No.ObservedPredicted 1125.3110.3104.980.5 2123.1109.3104.278.8 3119.4106.0103.676.5 4118.1103.7102.675.1 5118.5108.1101.770.4 6115.698.896.161.9 7113.4104.7104.065.2 8117.394.990.457.1 9112.296.390.254.3 Observed Strength higher than most predicted for all the combinations of the fly ash

42. Percent Influence Prasanth Tanikella - Purdue University42 Phase 3 Observed Strength higher than most predicted for all the combinations of the fly ash Set Time SulfateAluminaGlassError 26.817.0714.152.01 Peak Heat SpsurfaceSAFGlassError 39.4637.762.8419.94 Time Peak SpsurfaceMeansizeMgoError 63.444.20.7731.59 Wn 28 Day BlainesCarbonAluminaError 49.8810.9114.3524.85 SAI 28 Day MeansizeSulfateSAFError 66.6215.13.7715.52

43. Summary - Phase 3 Ternary Binder Systems Prasanth Tanikella - Purdue University43 Phase 3 None of the properties were found linearly additive Reasons could be: 1.Variables chosen in the binary binder systems can not explain a significant variation in the dependent variables 2.A few of the binary binder models were not significant and the error carried into the analysis of the ternary binder systems 3.The chosen variables might not be “linearly” related to the properties of the binary binder systems Weighted linear combinations of strength activity index at 28 days suggest a synergistic effect in the addition of two ashes to the binder system Physical properties of the fly ashes were more influencing than the chemical composition in most of the properties Surface area of fly ashes has the highest effect on the properties The most influencing variables on ternary binder systems were similar to the ones for binary binder systems

44. Conclusions Prasanth Tanikella - Purdue University44 Class C and Class F ashes were significantly different in both their physical characteristics and chemical composition There was significant difference in the effect of the two classes on binder properties Both physical and chemical characteristics of fly ash had an effect on the binder systems The sets of variables affecting each of the properties were unique The signs of the coefficients in the models indeed pointed out the type of effect on the property The statistical analysis of the properties of binary binders allowed us to draw inferences about the characteristics of fly ash which held the highest importance

45. Conclusions Prasanth Tanikella - Purdue University45 Some of the properties could not be accurately predicted by the statistical models with good significant as there were errors introduced by the limited number of variables chosen for modeling Statistical analysis on the properties of ternary systems indicated that these properties are not a weighted linear combination of binary binder models The statistical analysis of the properties of the ternary systems allowed us to draw inferences about the most significant variables and also about their relative percent influence Specific surface area of the fly ash had the highest impact on all the properties of binder systems

46. THANK YOU 46Prasanth Tanikella - Purdue University

47. Results Chemical composition of fly ashes PROPERTY CaO(%)SiO 2 (%)Al 2 O 3 (%)Fe 2 O 3 (%)Sulfate (%) Alkali Content as Na 2 O (%) LOI(%) FLY ASHClass PetersburgF*F*1.8643.8221.7425.290.872.291.39 Elmer smithF*F*9.3141.617.7422.020.602.322.37 TrimbleF2.546.9121.0819.91.092.451.89 Miami 8F*F*3.9855.5226.024.620.762.552.43 Mill creekF*F*5.4247.4819.9918.520.692.551.38 ZimmerF*F*4.9438.6618.9624.92.081.441.48 Miami 7F*F*1.2555.8929.454.960.422.202.31 RockportC16.9843.6521.766.580.452.080.9 JoppaC*C*26.2335.7518.016.360.072.310.35 KenoshaC23.3537.7820.115.870.532.180.38 MillerC*C*24.6236.3818.746.030.522.080.44 HennepinC*C*21.840.3619.385.910.351.990.61 JolietC*C*26.9832.1217.886.411.283.920.49 VermilionC*C*23.9239.1318.776.190.221.910.43 Will countyC*C*26.9732.318.556.470.433.060.35 Rush islandC27.6634.2316.916.860.052.260.17 BaldwinC*C*25.2335.0619.396.250.282.240.49 LabadieC24.2637.0319.286.461.141.940.25 SchaferC*C*20.2941.919.326.760.481.830.44 EdwardsC*C*24.2833.1519.2110.110.751.630.43 * INDOT list of approved fly ashes Phase 1 47Prasanth Tanikella - Purdue University

48. Results Physical characteristics of fly ashes PROPERTY Blaine’s Specific surface (cm 2 /g) Mean Size (microns) Specific Surface - LPSD (cm 2 /g) Strangth activity index (%) Magnetic particles (%)Specific Gravity FLY ASHClass PetersburgF*F*239128.379849104.337.722.63 (2.55) Elmer smithF*F*309233.246344109.932.992.64 (2.52) TrimbleF325327.358857109.126.392.69 Miami 8F*F*360031.5813012112.34.182.22 (2.21) Mill creekF*F*373926.3510295125.724.92.60 (2.46) ZimmerF*F*378226.11130896.235.322.68 (2.64) Miami 7F*F*408830.4112592118.23.682.26 (2.22) RockportC435432.211963134.13.52.56 JoppaC*C*437118.3717597135.90.312.72 (2.70) KenoshaC445217.3516577121.202.80 MillerC*C*485124.931708912302.63 (2.66) HennepinC*C*512516.8816457136.50.072.70 (2.35) JolietC*C*535614.4819776116.702.84 (2.46) VermilionC*C*553613.8517928136.70.122.69 (2.64) Will countyC*C*590714.8519646140.202.84 (2.49) Rush islandC592420.7717477127.702.81 BaldwinC*C*610221.9915492127.202.72 (2.66) LabadieC626916.6916503118.62.892.75 SchaferC*C*642818.8714679118.82.72.58 (2.59) EdwardsC*C*730615.08220751333.342.63 (2.65) 48 * INDOT list of approved fly ashes.SG listed in () indicate values from INDOT’s list of approved fly ashes. Phase 1

49. Analysis Andreasen Pipette Analysis  An attempt was made to resolve the differences in the observed PSD using the Andreasen Pipette  Particles suspended in dispersing solution  Particles settle at different rates. The rates depend on the radius and density of the particles  Stokes law used to calculate the particle size Prasanth Tanikella - Purdue University49 Phase 1

50. Results Morphology General Inferences  Fly ash particles were generally spherical in shape  Fly ash particles were found inside some of the hollow spherical particles  A few pieces of carbon (usually of a very large size) can be seen with a “Swiss Cheese” structure  Particles with rugged surface were generally magnetic in Class F ashes, contrary to the class C fly ashes which contained sulfate, magnesium and sodium Prasanth Tanikella - Purdue University 50 Phase 1

51. Phase 2 - Evaluation of the hydration characteristics of cement-fly ash binder systems When used as a substitute for part of the cement, fly ash offers a lot of benefits, both in terms of early and later hydration characteristics and in terms of the economy As seen from Phase 1, no two fly ashes are entirely similar with respect to their chemical and physical properties As a consequence, their incorporation into cementitious binder systems can result in highly variable hydration related characteristics It is important to understand and estimate the properties of cement-fly ash binder systems for its field application In addition, we can also estimate the amount and type of fly ash(s) to be added to the binder system to achieve some required properties using models that account for variable characteristics of the fly ashes Prasanth Tanikella - Purdue University51

52. Initial Setting Time  Initial setting time experiments were performed according to ASTM C 191  Set time value of a binary binder (fly ash + cement) – Average of duplicates  Manual vicat needle was used for the measurements  Water/binder ratio selected based on the consistency of the binder (ASTM C 187) Mixing process  Dry mixing of the powder by hand  Rest of the procedure, as mentioned in the standard Prasanth Tanikella - Purdue University52 Phase 2

53. Calcium Hydroxide Content at 7 days - Results  At 7 days, the amount of calcium hydroxide in fly ash pastes is similar or higher than that in plain paste Prasanth Tanikella - Purdue University53 Phase 2

54. Strength Activity Index at 3 days - Results  Most of the ashes show a lower strength at 3 days compared to plain cement paste Prasanth Tanikella - Purdue University54 Phase 2

55. Strength Activity Index at 7 days - Results  Few of the Class C ashes show a higher strength at 7 days compared to plain cement paste (could be due to the inception of hydration in fly ashes)  No pozzolanic reaction at this age  Difference in the rates of reactions in the fly ashes can be clearly observed Prasanth Tanikella - Purdue University55 Phase 2

56. Strength Activity Index at 28 days - Results  Correlation between strength activity index at 28 days, measured with two different cements  R 2 = 0.84 Prasanth Tanikella - Purdue University Phase 2 56

57. ANOVA Table (Class C Ashes) – Set Time Phase 2 SourceDFSum of SquaresMean SquareF Valuep-Value Model33.2691.0891.650.2543 Error85.2920.6615 Total118.561 R2R2 0.3818 Adj - R 2 0.15 VariableDF Parameter Estimate Standard Errort-Valuep-Value Intercept14.4564.1121.080.3101 sulfate11.1780.6440.1830.1048 alumina1-0.0850.235-0.360.7267 glass1-0.5830.619-0.940.3738 Prasanth Tanikella - Purdue University57

58. ANOVA Table (Class F Ashes) – Set Time Phase 2 SourceDFSum of SquaresMean SquareF Valuep-Value Model30.443580.147861.630.3487 Error30.271890.09063 Total60.71547 R2R2 0.62 Adj - R 2 0.24 VariableDF Parameter Estimate Standard Errort-Valuep-Value Intercept11.260930.998261.260.2958 sulfate10.469460.252331.860.1598 alumina10.073250.07690.950.4111 glass1-0.08450.53944-0.160.8855 Prasanth Tanikella - Purdue University58

59. ANOVA Table (Class C Ashes) – Set Time Phase 2 SourceDFSum of SquaresMean SquareF Valuep-Value Model33.2691.0891.650.2543 Error85.2920.6615 Total118.561 R2R2 0.3818 Adj - R 2 0.15 VariableDF Parameter Estimate Standard Errort-Valuep-Value Intercept14.4564.1121.080.3101 sulfate11.1780.6440.1830.1048 alumina1-0.0850.235-0.360.7267 glass1-0.5830.619-0.940.3738 Prasanth Tanikella - Purdue University59

60. ANOVA Table (Class F Ashes) – Set Time Phase 2 SourceDFSum of SquaresMean SquareF Valuep-Value Model30.443580.147861.630.3487 Error30.271890.09063 Total60.71547 R2R2 0.62 Adj - R 2 0.24 VariableDF Parameter Estimate Standard Errort-Valuep-Value Intercept11.260930.998261.260.2958 sulfate10.469460.252331.860.1598 alumina10.073250.07690.950.4111 glass1-0.08450.53944-0.160.8855 Prasanth Tanikella - Purdue University60

61. ANOVA Table (Class C Ashes) – (SAI) at 7 days Phase 2 Prasanth Tanikella - Purdue University SourceDF Sum of Squares Mean SquareF Valuep-Value Model3873.541291.1803374.0173620.0514 Error8579.843972.480485 Total111453.385 R2R2 0.601 Adj - R 2 0.4514 VariableDF Parameter Estimate Standard Errort-Valuep-Value Intercept1-521.432308.59493-1.68970.1296 SAF15.867393.057111.919260.0912 cao19.321634.975221.8736120.0979 glass117.941117.889172.2741440.0525 61

62. ANOVA Table (Class F Ashes) – (SAI) at 7 days Phase 2 Prasanth Tanikella - Purdue University SourceDFSum of SquaresMean SquareF Valuep-Value Model3229.1683876.389465.550.1565 Error227.5357513.76788 Total5256.70413 R2R2 0.8927 Adj - R 2 0.7318 VariableDF Parameter Estimate Standard Error t-Valuep-Value Intercept1-234.525489.77232-2.612450.1206 SAF13.449570.983773.506480.0726 cao15.084651.327913.8290620.0619 glass1-1.299993.55845-0.365320.7499 62

63. ANOVA Table – Heat of Hydration Phase 2 Prasanth Tanikella - Purdue University 63 Modelp-valueR2R2 Adj-R 2 VariablesStatistic PeakHeatClass C0.04840.56620.4216spsurfaceSAFGlass -0.000291-0.1680.6817 Coefficient 0.00840.01720.1447 p-value Class F0.45640.53460.0685spsurfaceSAFGlass -0.0000264-0.099520.6113 Coefficient 0.18680.20570.2479 p-value TimePeakClass C0.17220.41030.2138spsurfacemeansizeSAF -0.0301-10.128763.35305 Coefficient 0.05330.13020.1064 p-value Class F0.06980.87780.7556spsurfaceMeansizeSAF -0.0145-12.491413.5769 Coefficient 0.05970.06560.2062 p-value TotalHeatClass C0.05620.64610.4692meansizeCarbonSAFcao 1.42925-37.928-3.8206-4.764Coefficient 0.01510.12960.22590.3643p-value Class F0.51010.69990.0998meansizeCarbonSAFcao -2.251332.8216-4.8452-3.2055Coefficient 0.81690.52480.54380.7719p-value

64. ANOVA Table – Calcium Hydroxide Content Phase 2 Prasanth Tanikella - Purdue University 64 Modelp-valueR2R2 Adj-R 2 Variables Statistic CH 1 DayClass C0.05650.59090.4375blainescarbonalumina 0.00019180.072570.02428 Coefficient 0.01090.88610.7195 p-value Class F0.04580.96920.9231blainescarbonalumina -0.000006-0.41087-0.0276 Coefficient 0.89980.0170.0484 p-value CH 7 DaysClass C0.01710.70130.5893blainescaoglass 0.0003240.059581.03531 Coefficient 0.02680.37550.0111 p-value CH 28 DaysClass C0.01350.7190.6136blainesspsurfacesulfate 0.00052330.00004180.38429 Coefficient 0.00210.42520.1818 p-value Class F0.16020.89010.7253blainesspsurfacesulfate -0.00028860.00014690.29562 Coefficient 0.2270.0930.1728 p-value

65. ANOVA Table – Non-evaporable Water (W n ) Phase 2 Prasanth Tanikella - Purdue University 65 Modelp-valueR2R2 Adj-R 2 Variables W n 1 DayClass C0.06840.56940.4079blainescarbonaluminaStatistic 0.000132-0.64483-0.01391Coefficient 0.03720.18740.8209p-value W n 3 DaysClass C0.03330.64430.511sulfateSAFmgoStatistic 0.32747-0.014680.04352Coefficient 0.01350.44830.7228p-value Class F0.15760.8920.7299sulfateSAFmgoStatistic 1.048840.02403-0.32493Coefficient 0.06930.35050.1065p-value W n 28 DaysClass C0.16180.7190.6136blainescarbonaluminaStatistic 0.000220.639460.00992Coefficient 0.05310.45380.929p-value

66. Test for Additivity – Set Time (minutes) Prasanth Tanikella - Purdue University66 Phase 3 Exp.No Model 1Model 2Model 3 ObservedPredicted 1 120157.4157.5128 2 155150.2162.4210.8 3 160156.8168.9210.2 4 195151.5153.8147.2 5 125152.9165.226 6 230173.6168.3177.9 7 170144.7152.4119.2 8 225151.5151.6129.3 9190153.2161122.7 Observed set time was not the same as predicted set time for most of the cases