Consolidacion

CONSOLIDACION

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LABORATORY TESTING

The coefficient of consolidation (cv ) can be evaluated using information obtained from the laboratory consolidation test if, each time you put a load increment on the sample, you monitor the change in the height of the sample over time.

As discussed in the last class, cv transforms a plot of U as a function of T into a plot of settlement as a function of time since

U =

s t)×100

and T =

cv

t

H 2

s

c

dr

Here Hdr is half the specimen height since most consolidation test specimens are doubly drained and sc is the total consolidation settlement under the load increment. Therefore, it should be possible to derive a value for cv from the settlement history of the specimen under a given load increment. Unfortunately, things are not that simple.

When you put each new load increment on the sample, the sample undergoes some small amount of immediate elastic settlement, then primary consolidation settlement, then secondary compression settlement.

It is very difficult to determine where each phase ends and the next begins, so it is very difficult to determine what settlements correspond to 0% and 100% consolidation. In other words, you really don’t know s(t) or sc very well.

Today we’ll look at several methods for finding cv from laboratory test data. Each method is designed to get around the uncertainty in determining where consolidation settlement begins and ends during the test.

Casagrande Method

Begin by plotting the change in specimen height (or dial gage reading) as a function of the logarithm of time.

To estimate the dial gage reading corresponding to 0% consolidation (R0) you can make use of the fact that the plot of U as a function of T is parabolic for U < 60%.

1. Pick two times t1 and t2 in the ratio of 4:1 and note the corresponding dial readings R1 and R2 (which represent the height of the sample at the two different times).

1. If the curve is parabolic and times t1 and t2 differ by a factor of 4, then R1 and R2 must differ by a factor of 2. If R2 is exactly twice R1, then

R2 – R1 = R1 – R0

This means that R0 is as far above R1 as R1 is above R2. This lets you locate R0 on the plot by scaling off the distance between R1 and R2.

1

________________

Since we’re dealing with laboratory data that contains some measurement error, it’s a good idea to repeat Steps 1 and 2 a couple of times using different values of t1 and average the results.

0%

10%

d

20%

30%