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Publication Number: FHWA-RD-03-060

Concrete Mixture Optimization Using Statistical Methods

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CHAPTER 4 Laboratory Experiment Using Factorial Approach

4.1 Introduction

This chapter describes the application of factorial experiment design to the problem of concrete mixture optimization. In a mixture, the total amount (mass or volume) of the product is fixed, and the settings of each of the q components are proportions. Because the total amount is constrained to sum to one, the component variables are not independent. However, the q components can be reduced to q-1 mathematically independent variables using the ratio of two components as an independent variable [8]. In the case of concrete, w/c is a natural choice for this ratio variable. If this strategy is adopted, a factorial approach may be applied.

4.2 Selection of Materials, Proportions, and Constraints

The materials used in this experiment were identical to those used in the mixture experiment (chapter 3): Type I cement, tap water, #57 crushed limestone, natural sand, silica fume (slurry), and naphthalene-sulfonate-based HRWRA. The six components were reduced to five independent variables: x1 = w/c (by mass), x2 = fine aggregate volume fraction, x3 = coarse aggregate volume fraction, x4  = HRWRA volume fraction, x5 = silica fume volume fraction.

The volume fraction ranges for coarse aggregate, fine aggregate, HRWRA, and silica fume were the same as used in the mixture experiment. The range of w/c (by volume) was calculated from the volume fraction limits of the mixture experiment. The lower limit of w/c was 0.16 ÷ 0.15 = 1.067, and the upper limit was 0.185 ÷0.13 = 1.423. These limits were equated to coded limits of -1.5 to +1.5 to give the most similar experimental region to that of the mixture experiment. Table 9 shows the settings of each variable corresponding to coded values of -1 and +1.

Table 9. Variable settings corresponding to coded values (factorial experiment)

VariableID Low Setting (coded value = -1)High Setting (coded value = 1)
w/c ratiox11.1263 (@ 0.36 by mass)1.3637 (@ 0.43 by mass)

Fine aggregate

x20.25710.2853

Coarse aggregate

x30.40710.4353

HRWRA

x40.00510.0069

Silica fume

x50.01530.0247

For the aggregates, HRWRA, and silica fume, volume fractions were converted to batch masses (batch volume for HRWRA) based on total batch size and material properties. The batch masses of water and cement were calculated by constraining the sum of volume fractions of all six components to sum to one. This gives two equations in two unknowns (the other equation being the w/c expressed volumetrically) that can be solved for volume fraction of water and volume fraction of cement. Entrapped air was ignored in these calculations, although in practice it will affect yield calculations and an overall adjustment to the mixture proportions may be necessary for proper yield.

The performance criteria for the mix were as follows: slump between 50 and 100 mm, 1-day strength greater than 22 MPa, 28-day strength greater than 51 MPa, and charge passed in the RCT less than 700 coulombs. These are the same performance criteria used for the mixture experiment, except that the 1-day and 28-day strengths were rounded to 22 and 51 MPa (from 22.06 and 51.02 MPa used in the mixture experiment)1.

4.3 Experiment Design Details

A central composite design (see chapter 2) was chosen for this experiment. The actual values for each variable (expressed in terms of volume fraction) corresponding to the coded levels ±1 are shown in table 10. A commercially available software package for experiment design and analysis was used to plan the experiment. Thirty-one batches were planned in two nearly orthogonal blocks. The first block consisted of a half-fraction of 16 factorial points2 and 3 center points, and the second block consisted of 10 axial points and 2 center points. A total of five center points was chosen to allow use of the center point mixes as statistical control mixes to assess week-to-week variation, if any, over the five weeks of mixing (in addition to the use of center points as replicates to estimate pure error). The run order within each block was randomized to reduce the effect of uncontrolled variables. Center point runs were placed first and last (based on the total number of runs) with the remainder equally spaced, resulting in three center points in the factorial block and two center points in the axial block. The mixture proportions for each batch are shown in table 10.

One batch (run 3) was repeated at the end of the experiment because of suspiciously low strength values. A center point batch was included with the repeat to check statistical control. The results of the repeated batch were used in subsequent analyses.

4.4 Specimen Fabrication and Testing

The materials used in this study included a portland cement conforming to ASTM specification C150-94 Type I/II, a #57 crushed limestone coarse aggregate meeting grading requirements of AASHTO M43, a natural sand fine aggregate, silica fume (in slurry form), a naphthalene-sulfonate based HRWRA (ASTM C494-98 Type F/G), and municipal tap water. Thirty-one batches of concrete, each approximately 0.04 m3 in volume, were prepared over a 6-week period. Each batch was prepared in accordance with mixing procedures set forth in ASTM C192-95.

Table 10. Mixture proportions (per m3) for factorial experiment

Batch (run) w/cFine Agg (kg)Coarse Agg (kg)HRWRA (L)Silica Fume (kg)Cement (kg)Water (kg)

1

0.36755.71098.76.9054.3408.7146.1
20.36755.71174.85.1054.3369.6132.2
30.40718.41136.86.0044.0394.9156.1
40.40718.41136.86.0044.0394.9156.1
50.43681.01098.76.9054.3405.2175.4
60.43755.71098.75.1054.3370.1160.2
70.43755.71098.76.9033.6380.2164.6
80.43681.01098.75.1033.6420.1181.9
90.36681.01098.75.1054.3453.1162.0
100.40718.41136.86.0044.0394.9156.1
110.43755.71174.85.1033.6345.0149.4
120.36681.01098.76.9033.6464.4166.0
130.43681.01174.85.1054.3370.1160.2
140.36755.71098.75.1033.6425.3152.1
150.43681.01174.86.9033.6380.2164.6
160.36755.71174.86.9033.6380.9136.2
170.36681.01174.86.9054.3408.7146.1
180.36681.01174.85.1033.6425.3152.1
190.43755.71174.86.9054.3330.1142.9
200.40718.41212.96.0044.0355.4140.5
210.47718.41136.86.0044.0357.2168.1
220.40793.11136.86.0044.0355.4140.5
230.40718.41136.87.8044.0392.4155.1
240.40718.41136.86.0044.0394.9156.1
250.32718.41136.86.0044.0441.7141.3
260.40718.41136.84.2044.0397.5157.1
270.40718.41136.86.0023.3408.1161.3
280.40718.41060.66.0044.0434.5171.7
290.40718.41136.86.0044.0394.9156.1
300.40718.41136.86.0064.7381.8150.9
310.40643.71136.86.0044.0434.5171.7

Precautions were taken to compensate for mortar retained by the mixer, by "buttering" the mixer prior to preparing each batch. The concrete was mixed using a rotating-drum machine mixer with a 0.17 m3 mixing capacity.

Each batch included sufficient concrete for 2 slump tests, 1 unit weight and air content test (ASTM C138 and ASTM C231), and 10 100 mm x 200 mm cylinders. All cylinders were fabricated in accordance with ASTM C192, except that external vibration (a vibrating table) was utilized for consolidation in lieu of rodding when slump was less than 50 mm. Immediately following casting, the cylinders were covered with plastic and left in the molds at room temperature for 24 ± 8 hours, after which they were removed from the molds and moist cured at 23 ± 2 ºC until testing. Specimens were tested for compressive strength in accordance with ASTM C39 at the ages of 1 and 28 days. In most cases, three cylinders were tested at each age, however, where anomalies in either specimen condition or test results were evident, a fourth or fifth specimen may have been tested. Prior to compression testing, the ends of each cylinder were ground plane and parallel in accordance with ASTM C39 tolerances. Three of the remaining cylinders from each batch were used for the RCT testing. Testing was performed according to ASTM C1202, except that the 50 mm test specimens were cut from the middle of each cylinder instead of from the top. All RCTs were performed at an age of 42 days from casting.

4.5 Results and Analysis

4.5.1 Responses

The average values for slump, 1-day strength, 28-day strength, and charge passed (RCT) for each batch are shown in table 11, along with the estimated cost (dollars per cubic meter) of the concrete mixture. The costs were calculated from the mixture proportions for each batch, based on approximate component costs obtained from a local (mid-Atlantic) ready-mix concrete producer. Each response was analyzed individually by examining summary plots of the data (scatterplots and means plots), fitting a model using ANOVA and least-squares methods, validating the model by examining the residuals for trends and outliers, and interpreting the model graphically. A detailed discussion of the analysis procedure for 1-day strength is presented in the following 2 sections. The analyses for the other responses were performed in a similar manner. The models for the other responses are reported in section 4.5.4.

4.5.2 Exploratory Data Analysis for 1-Day Strength

One advantage of the factorial approach is the ability to perform an initial assessment of the data using graphical techniques such as raw data plots, means plots, scatterplots, and cube plots. These techniques are illustrated in figures 12-15 (for complete sets of plots for each response, see appendix B). A raw data plot of 1-day strength is shown in figure 12. This plot illustrates the variation in the response (1-day strength) over time, for each run. The control batch results, shown as hollow squares, give an indication of consistency over time. In this case, the control results are all about equal, indicating no time-related effects. Raw data plots are also helpful in identifying suspect data values, which may result from (for example) errors in data recording or data entry, equipment malfunction, or poor specimen fabrication.

Table 11. Test results and costs (factorial experiment)

Batch
(run)
Slump
(mm)
1 Day
(MPa)
28 Day
(MPa)
RCT
(coul)
Cost
($/m3)
17316.358.5286119.77
24422.659.8209113.93
31320.852.6160107.71
410216.560.4296107.71
55716.455.0257118.49
614313.658.6541112.96
76712.950.450299.21
81320.352.423497.83
98618.463.0305118.75
1010215.254.8445107.71
1114020.762.341293.70
123217.156.2252105.09
131324.254.2341112.98
147617.250.353499.24
151321.359.227899.23
161320.960.5206100.27
175718.856.6315119.78
182921.958.335599.26
193516.162.9230114.36
203819.058.6211105.49
2111714.553.9458104.63
226717.862.4294105.48
236419.967.5268111.16
241626.458.5189107.71
257919.057.0257111.52
266419.150.9273104.27
2715216.854.470590.56
289520.253.3307109.93
293518.455.2162107.71
3010217.450.4332124.87
317618.455.2277109.95

Figure 12 shows a raw data plot for 1-day strength. The response (strength) is on the Y-axis, and the run number (1 to 31 in this case) is on the X-axis. For each control run, the individual strength test results are plotted as hollow squares. For all other runs, the results are plotted as solid triangles. The raw data plot allows detection of incorrect data entry, outliers or time-dependent trends in the data.

Figure 12. Raw data plot for 1-day strength (factorial experiment)

Figure 13 is a scatterplot showing the effects of varying one factor (w/c) on the 1-day strength. In this case, as expected, the 1-day strength decreases with increasing w/c.

 Figure 13 shows an example of a scatterplot. This is an X-Y plot of 1-day strength (on the Y-axis) versus W/C (on the X-axis). The plot shows the measured response (in this case, 1-day strength) for each experimental run at the five different levels of the particular factor (in this case, W/C). The data points are shown as blue diamonds. A solid black best-fit line is also shown. In this case, the trend is decreasing 1-day strength with increasing W/C.

Figure 13. Scatterplot showing effect of w/c on 1-day strength (factorial experiment)

Figure 14 shows a means plot for 1-day strength. The means plot allows comparison of the effects of each factor. In this experiment, 1-day strength was clearly influenced by w/c. Other factors appear to have had little effect.

Figure 14 shows an example of a means plot for 1-day strength. The response (1-day strength) is on the Y-axis and the factors (independent variables) are on the X-axis in the following order: W/C, CA (coarse aggregate), FA (fine aggregate), HRWRA, and SF (silica fume). The X-axis is not quantitative for these plots. For each factor (on the X-axis) the mean responses at each level of the factor are plotted as diamonds and are connected by a solid line. Thus, there are five solid lines on the plot. The levels for each factor are in order from the lowest to the highest (that is, negative 2, negative 1, 0, 1, 2). The levels are not indicated explicitly on the X-axis but are understood to read from left to right for each factor. These plots allow for assessment of potentially significant effects.

Figure 14. Means plot for 1-day strength (factorial experiment)

Finally, figure 15 shows a cube plot of 1-day strength with respect to three factors (w/c, fine aggregate, and coarse aggregate). A cube plot is a convenient means of assessing quantitative effects of three factors on a response.

Figure 15 shows a cube plot for a factorial experiment. The plot is drawn as a cube with three different variables (factors) on each axis (X, Y, Z) of the cube. In this particular example, W/C is on the X-axis, fine aggregate is on the Y-axis, and coarse aggregate is on the Z-axis (into the page). On the plot the variables are designated A, B, and C, respectively. Each vertex of the cube represents a combination of low or high settings of the variables. For example, the lower left front vertex is (A negative, B negative, C negative), the low settings for all three variables. The lower right front vertex is (A positive, B negative, C negative) with high setting for W/C and low for the others. The response value (in this case, 1-day strength) measured at the particular settings is plotted at each vertex. The plot can be used to assess effects of the variables and interactions between them.

Figure 15: Example of cube plot of factorial points

4.5.3 Model Fitting and Validation for 1-Day Strength

After assessing the data graphically, the second step in analysis is to estimate an appropriate model for each response. As with the mixture approach, ANOVA and least-squares regression techniques are used. The first step is to use ANOVA to determine the appropriate type of model (e.g., linear, quadratic). An ANOVA table for sequential model sum of squares, shown in table 12, suggests that both linear and quadratic terms are significant. A lack-of-fit ANOVA table (table 13) suggests that a quadratic model has insignificant lack-of-fit ("Prob > F" = 0.8339).

Table 12. Sequential model sum of squares for 1-day strength (factorial experiment)

SourceSum of SquaresDFMean SquareF ValueProb > F
Mean10780.54110780.54--
Linear 215.97543.1922.15< 0.0001
2FI27.35102.731.920.1236
Quadratic13.6052.723.480.0441
Cubic (aliased) 1.8650.370.310.8865
Residual 5.9551.19--
Total11045.2631356.30--

Table 13. Lack-of-fit test for 1-day strength (factorial experiment)

SourceSum of SquaresDFMean SquareF ValueProb > F
Linear43.98212.091.750.3129
2FI16.63111.511.270.4444
Quadratic3.0360.510.420.8339
Cubic (aliased)1.1811.180.980.3772
Pure Error4.7841.19  

When a central composite design is used, the full quadratic model can be estimated, but often some of the terms are not significant. The following procedure3 was used to identify an appropriate reduced quadratic model:

1) Fit the full quadratic model and for each coefficient, calculate the t-statistic for the null hypothesis that the coefficient is equal to zero.

2) Perform the regression again with a partial model containing only those terms that are statistically significant (i.e., those terms that had a t-statistic greater than that for the chosen significance level, in this case 0.05). Calculate the t-statistics again and drop any terms which are not significant.

3) Repeat step 2 until the partial model contains only significant terms.

4) Add first-order terms required to make the model hierarchical. Hierarchical polynomial models make sense under linear transformations such as changing units of temperature from Celsius to Fahrenheit [11]. All second-order terms that appear in the model must have corresponding first-order terms included in order to make the model hierarchical.

The ANOVA for the final hierarchical model (with the hierarchical terms shaded) is shown in table 14.

Table 14. ANOVA for 1-day strength model (factorial experiment)

SourceSum of SquaresDFMean SquareF ValueProb > F
Model240.87830.1127.76< 0.0001
A213.261213.26196.66< 0.0001
B0.4810.480.450.5113
C0.0410.040.040.8433
E2.0612.061.900.1819
A26.2016.205.720.0257
AC5.1515.154.750.0404
AE7.1617.166.600.0175
BC6.5116.516.000.0227
Residual23.86221.08--
Lack of fit19.08181.060.890.6248
Pure error4.7841.19--
Corr. total264.7230---

For one-day strength, y2, the fitted model was:


Equation 17:
Equation 17. Lowercase Y tophat subscript 2 equals negative 63.8 minus 860.8 times lowercase X subscript 1, plus 1,361.3 times lowercase X subscript 2, plus 450.8 times lowercase X subscript 3, minus 1,431.5 times lowercase X subscript 5, plus 323.9 times lowercase X subscript 1 squared, plus 1,068 times lowercase X subscript 1 times lowercase X subscript 3, plus 3,780 times lowercase X subscript 1 times lowercase X subscript 5, minus 3,208 times lowercase X subscript 2 times lowercase X subscript 3.

The adequacy of each fitted model was validated quantitatively by calculating statistical measures such as residual standard deviation and PRESS, and graphically by examining residual plots. The residual standard deviation, s, for this model is 0.99 MPa. A value of s near the repeatibility value (replicate standard deviation calculated from center points) is an indication of an adequately fitting model. For this experiment, the repeatibility value is 1.04 MPa, which is close to s. The PRESS statistic (see page 12) is a measure of how well the model fits each point in the design (the smaller the PRESS statistic, the better the fit). PRESS and some other quantitative indicators of model adequacy are shown in table 15.

Table 15. Summary statistics for 1-day strength model (factorial experiment)

Std. dev.1.04
Mean18.65
C.V.5.58
PRESS45.16
R-squared0.9099
Adjusted R-squared0.8771
Predicted R-squared0.8294

The residuals are the deviations of the observed data values from the fitted values, xI , and are estimates of the error terms ei in the model. The ei are assumed to be random and normally distributed with mean equal to zero and constant standard deviation. A normal probability plot of the residuals (shown in figure 16) is used to assess the validity of this assumption. If the error terms follow a normal distribution, they will fall on a straight line on the normal probability plot. Because they are estimates of the error terms, the residuals should exhibit similar properties.

If the assumptions are valid, plots of the residuals versus run sequence, predicted values, and other independent variables should be random and structureless. If structure remains in the residuals, residual plots may suggest modifications to the model that will remove the structure.

Figure 16 shows an example of a normal probability plot of residuals, which is used for model validation. This plot is an X-Y plot with normal probability percentages plotted on the Y-axis and the residuals for each run plotted on the X-axis. If the residuals are normally distributed, they will fall on a straight line. Therefore, the validity of the assumption that the residuals are normally distributed can be assessed using this plot.

Figure 16: Example of a normal probability plot for model validation

Figure 17 shows a plot of residuals versus run sequence for 1-day strength. The plot shows no significant structure to the residuals.

Figure 17 shows an example of a residuals versus run plot used for model validation. The residuals are plotted on the Y-axis and the run number is plotted on the X-axis. The residual values are plotted in run order, revealing any trends in residuals over time. Ideally, the plot should show no trends or structure.

Figure 17. Example of a resident plot (residuals vs. run) for model validation

4.5.4 Models for Other Responses

Using the same procedure described above for 1-day strength, the following models were fit to slump (x1), 28-day strength (x3), and 42-day RCT (x4) results:

Equation 18:
Equation 18. Lowercase Y tophat subscript 1 equals negative 1,365.5 minus 4,876.9 times lowercase X subscript 1, minus 8,334.7 times lowercase X subscript 2, plus 8,256.5 times lowercase X subscript 3, plus 6.698 times 10 to the fifth power times lowercase X subscript 4, minus 4,503.6 times lowercase X subscript 5, plus 20,185 times lowercase X subscript 1 times lowercase X subscript 2, minus 1.564 times 10 to the sixth power times lowercase X subscript 3 times lowercase X subscript 4.
Equation 19:
Equation 19. Lowercase Y tophat subscript 3 equals 124.0 minus 227.3 times lowercase X subscript 1, plus 3,390.0 times lowercase X subscript 4, minus 3,937.5 times lowercase X subscript 5, plus 10,262 times lowercase X subscript 1 times lowercase X subscript 5.
Equation 20:
Equation 20. Lowercase Y tophat subscript 4 equals 635.4 plus 4,445.6 times lowercase X subscript 1, minus 1,199.8 times lowercase X subscript 2, minus 1,548.5 times lowercase X subscript 3, minus 31,651 times lowercase X subscript 5, plus 1.635 times 10 to the sixth power times lowercase X subscript 5 squared, minus 1.448 times 10 to the fifth power times lowercase X subscript 1 times lowercase X subscript 5.

4.6 Optimization

The objective of optimization may be to find the "best settings" (settings which maximize or minimize a particular response or responses) or to meet a set of specifications. In either case, optimization usually involves considering several responses simultaneously. The same optimization strategy that was used in the classical mixture approach can be used for a factorial approach (see chapter 3).

4.6.1 Graphical Optimization

For three or fewer responses, contour plots can be useful in identifying optimum settings. Individual contour plots can be used to identify best settings for each response. Figure 18 shows a contour plot of 28-day strength as a function of w/c and silica fume with HRWRA at its middle setting. Figure 19 shows the same plot but with HRWRA at the high setting. These plots illustrate that the highest strength is reached at the high level of HRWRA coupled with the low levels of w/c and silica fume.

Contour plots can also be overlaid with the constraints for each response defining a subarea of settings that meet the response criteria. For example, figure 20 shows all settings meeting the following criteria: RCT < 700 and slump equal 50 to 100 mm. The white area in the plot indicates the settings meeting the criteria. The gray area on the plot shows the region of settings that do not meet the constraints simultaneously. Figure 21 shows the same plot with the added constraint that 28-day strength > 51 MPa. As constraints are added, the feasible region usually gets smaller. In some situations, no settings will meet all of the criteria.

Figure 18 shows a contour plot used for graphical optimization of a response. In this example, the response is 28-day strength and the variables are W/C on the X-axis and silica fume on the Y-axis (in general, there will be any two factors on the X- and Y-axes). For this particular plot, HRWRA is fixed at the middle setting (coded value 0). Contour lines indicating selected intervals of response values are shown. These contour plots are similar to topographic maps and can indicate maxima, minima, ridges, or directions of ascent/descent. Contour plots can be used to find the best settings of the factors subject to a given criterion or criteria.

Figure 18. 28-day strength in w/c and silica fume (HRWRA) at middle setting)

Figure 19 shows the same plot as figure 18 but with HRWRA at the high setting. The layouts of the plots are the same, except the contour values are different. At high W/C and low silica fume (lower right corner), strength is lowest. It increases steadily with lower W/C and higher silica fume to a point (W/C equals 0.38, silica fume equals 0.022, approximately). For W/C less than about 0.38, strength increases as W/C and silica fume decrease, with the highest predicted strengths at the lowest settings of W/C and silica fume. There is also a small increase in strength as W/C and silica fume increase together, but not as great as at the low settings. Comparing figure 19 to figure 18 indicates that the highest strength levels can be reached when HRWRA is at the high setting.

Figure 19. 28-day strength in w/c and silica fume (HRWRA at high setting)

Figures 20-22 show overlay plots used for graphical optimization of multiple responses. These plots are contour plots overlaid with constraints on responses. Figure 20 shows an overlay plot for silica fume (Y) and W/C (X) for the criteria RCT less than 700 and slump equals 50 to 100 millimeters. The gray regions in the upper left and lower right corners of the plot indicate areas where these criteria are not met. The white region between the gray regions shows the area in which the criteria are met.

Figure 20. Overlay plot for RCT <700 and slump = 50-100 mm

Figure 21 is an overlay plot adding the constraint 28-day strength greater than 51 megapascals to the plot in figure 20. Again, gray regions indicate settings that are out of the required ranges. The remaining "good" settings are shown in white. Lines indicate the boundaries of each constraint.

Figure 21. Overlay plot for RCT < 700, slump = 50 - 100 mm, and 28-day strength > 51 MPa

4.6.2 Numerical Optimization

For more than three responses considered simultaneously, numerical optimization is often more practical than graphical optimization. The numerical optimization technique using desirability functions described in chapter 2 and used in the classical mixture experiment (chapter 3) can also be applied to the factorial experiment. Desirability functions for this experiment are similar to those used in the mixture experiment, except that in some cases (e.g, cost) the endpoint values are different. The desirability functions are shown in figure 22.

Figure 22 is a graphical representation of the desirability functions used in the factorial experiment (for a detailed explanation of this figure, refer to the caption of figure 10). The figure shows a linear decreasing desirability function for cost, a triangular step desirability function for slump (target value), and rectangular step desirability functions for 1-day strength, 28-day strength, and RCT. Strength desirability functions had minimum constraints while the RCT has a maximum constraint.

Figure 22. Desirability functions for factorial experiment

Numerical optimization gave the following best settings for this concrete mixture: w/c = 0.367 (by mass), fine aggregate volume fraction = 0.285, coarse aggregate volume fraction = 0.408, HRWRA volume fraction = 0.0061, and silica fume volume fraction = 0.0153. The predicted response values and associated uncertainties (at a 95 percent confidence level) are slump = 74 ±19 mm, 1-day strength = 22.08 ±1.17 MPa, 28-day strength = 58.65 ±2.32 MPa, and RCT = 378 ±30 coulombs, at a cost of $100.68 per m3.

4.6.3 Accounting for Uncertainty

As described for the mixture approach, the fitted functions (models) and values predicted from them have uncertainty associated with them because they are estimated from data. For example, for the optimal mixture proportions shown above the predicted 1-day strength is 22.08 ±1.17 MPa. If these proportions are used, we can be 95 percent confident that the true 1-day strength will lie between 20.91 and 23.25 MPa. But because the specified 1-day strength is 22 MPa, it is quite possible that the true 1-day strength will fall below the specified value. Therefore, the constraints must be modified to account for the uncertainty in the fitted functions. The uncertainties for the optimal mixture can be used to define modified constraints, and a new set of optimal mixture proportions can be identified for these new constraints. The predicted responses based on the new optimal proportions must be checked to see that the original specifications are met.

The modified constraints are slump between 69 and 81 mm, 1-day strength greater than 23.17 MPa, 28-day strength greater than 53.32 MPa, and RCT less than 670 coulombs. The best mixture for this new set of constraints is w/c = 0.358 (by mass), fine aggregate volume fraction = 0.282, coarse aggregate volume fraction = 0.4071, HRWRA volume fraction = 0.0062, and silica fume volume fraction = 0.0153. The predicted response values and associated uncertainties (at a 95 percent confidence level) are slump = 74 ±20 mm, 1-day strength = 23.17 ±1.26 MPa, 28-day strength = 59.62 ±2.68 MPa, and RCT = 363 ±32 coulombs, at a cost of $101.65 per m3. All but one of the lower or upper bound values for the responses now meet the original specifications. The exception is the lower bound for 1-day strength, which is 21.91 MPa (compared with the specified value of 22 MPa). In practice, this small difference is probably insignificant; however, it may be worthwhile to investigate ways to increase 1-day strength for this mix. A slightly lower w/c (0.35, for example) would probably be a sufficient remedy. The predictive models estimated from the experiment can be used to predict responses for settings anywhere within the experimental design space (i.e., anywhere within the defined variable ranges). However, extrapolation beyond the design space is not recommended.

In the factorial experiment, accounting for uncertainty increased the cost of the optimal mix only slightly (from $100.68 to $101.65). As with the mixture experiment, the value of optimization is evident when the cost of the optimal mix ($101.65) is compared with the range of mixture costs in the experiment ($90.56 to $124.87).

 


1 The mixture experiment was originally performed using English units with 1-day strength requirement of 3200 psi and 28-day strength requirement of 7400 psi.

2 The 16 factorial points represent a half-fraction of the full factorial for 5 variables, which has 25 = 32 points. The 25-1 half-fraction can be used in this case because it is a Resolution V design that allows estimation of all linear coefficients and two-factor interactions without confounding.

3 This procedure may be used because the CCD experiment design is well-balanced and orthogonal (or nearly so).

 

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The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT).
The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT). Provide leadership and technology for the delivery of long life pavements that meet our customers needs and are safe, cost effective, and can be effectively maintained. Federal Highway Administration's (FHWA) R&T Web site portal, which provides access to or information about the Agency’s R&T program, projects, partnerships, publications, and results.
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United States Department of Transportation - Federal Highway Administration