Machine Learning applied to soil compaction.

In this article, we will use Machine Learning techniques to optimize soil compaction.

Soil is a material composed of a solid, liquid and gaseous part, which was formed during hundreds of millions of years of weathering and sedimentation. We understand compaction to eliminate the greatest amount of gaseous mass (commonly air) from the soil by loading.

The compaction curve consists of a curve that is constructed after marking the pairs of dry-density and moisture values of the sample. With a minimum of 5 points, a curve is drawn by hand that can correlate all these points and the maximum dry density and the optimal humidity at which this soil can be compacted are obtained.

These parameters are linearly independent and there is no relationship between the compaction test and soil classification, so two different tests are carried out to obtain it.

Problem description

For each kilometer of road, 5 test pits are required, of which two tests must be submitted:

• Soil classification
• Compaction

In practice, due to the economic resources and logistics involved, only 5 classification tests and 2 compaction tests are carried out and the results are interpolated.

Objective

Determine using Machine Learning techniques a correlation between soil classification and soil compaction, referring to the maximum dry density and optimal humidity value, to calculate the humidity at which a certain section of road will be compacted.

Model selection

Because the result is known and the problem is numerical, the models used were:

• Linear regression
• Multi-linear regression
• Polynomial regression

Techniques implemented

Dimension reduction

All columns that do not provide relevant information were reduced: 'Well', 'Side', 'Prf o (m)', 'Prf f (m)', '% Hum', 'ST >3', '3', 'AASTHO', '#', 'SUCS', '100%', '97%', '95%', '100%.1', '97%.1', '95%.1'

Missing values

In the LL column it was found that 30 data were missing, a histogram was used and it was determined that the normal curve that describes its data was offset from the middle, so the missing data was filled in with the median, which is 32.2

Graphics

All the necessary graphs were made to understand and interpret the problem.

Linear relationship (with sklearn)

2nd degree polynomial relationship (with numpy)

3rd degree polynomial relationship (with numpy)

Multi-linear relationship (with sklearn)

Multi-linear relationship (with sns) where the 14 parameters are related to each other and their relationship with the optimal Humidity.

Model evaluation

D max vs H opt

Linear regression:

2nd degree polynomial regression:

3rd degree polynomial regression:

Feature vs H opt

Linear regression:

Analysis of results

D max vs H opt

It can be seen that it is the polynomial regression that has a correlation coefficient closest to 1 and in the graph the one that best fits, which is why it will be the regression adopted

Feature vs H opt

A correlation coefficient value close to 1 can be observed and in the sns graphs the vast majority are linear relationships, so the multi-linear model achieved is accepted.

Conclusion

A correlation was determined between soil classification and soil compaction, to calculate the humidity at which a certain stretch of road will be compacted:

H opt = 0.117*(LL) + 0.0212*(LP) — 0.007*(2) + 0.010*(1 ½) — 0.004*(1) — 0.001*(¾) + 0.017*(⅜) — 0.001*(no 4) — 0.052*(no 10) +
+ 0.054*(no 40) + 0.036*(no 200) — 0.043*(% Grava) + 0.007*(% Arena) + 0.036*(% Finos) + 3.779

D max = 4.110*10^(-4)*(H opt)² — 4.256*10^(-2)*(H opt) + 2.394

WRITTEN BY

Mauricio Ramirez Salamanca

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