α

β

Aw

AK

1171 (563)

0.01232

0.4760

0.1647

1.5905

1206 (565)

0.002399

0.7134

0.0005713

2.6395

1232 (566)

0.002260

0.6790

0.001885

1.8129

Blended

0.026538

0.5933

0.0010507

3.5199

Stockpile

0.1735

0.3239

1647.1

0.7207

6.59 105

Taxiway A

0.1520

0.2690

2.9620

aubbase

Dense-graded

0.4961

0.3660

3.912

1.3930

stone

1.0729 10 6

Blended

1.0001

0.4444

5.8979

We determined point values of θu and *h*p in the

Figure 8 shows the relationship between degree

moisture retention test and fitted eq 1 to the ex-

of saturation and hydraulic conductivity for the

traction curve using a least squares approach to

materials tested and for the substitute materials.

determine the best fitted parameters *A*w and α.

Exponential regression curves fit to the data, which

Table 6 lists these parameters for the samples

include both extraction and absorption values, are

tested.

shown in Table 7.

displayed vs. pore water tension in Figure 7. Again,

the dashed line in Figure 7 is the best fit approxi-

mation used in the model to represent the unsatur-

ated hydraulic conductivity using the equation:

β

(2)

8.539 1021

1171 (563)

0.184

9.503 1022

1206 (565)

0.189

5.880 1020

1232 (566)

0.161

(cm/hr)

3.035 1011

hr)

Blended

0.114

1.061 109

Stockpile

0.100

1.981 1010

conductivity

Taxiway A

0.103

β = Gardner's exponent for hydraulic

subbase

conductivity.

8.3491 105

Dense-graded

0.050

We determined point values of *K*H and *h*p for

stone

each sample and fitted eq 2 to the extraction data

using a least squares approach to determine the

1.063 104

Blended

0.056

best fitted parameters *A*K and β (Table 6).

14