The grain-size distribution (GSD) curve is done by using Sieve analysis and Hydrometer analysis, then it is used to determine basic soil properties such as optimum moisture content (OMC), and maximum dry density (MDD), the suggested model is dependent on the retaining percent of soil from the (GSD) curve as independent variables.

In this research, appropriate steps techniques were employed to find the OMC and MDD of the soil. The data analysis is done by the SPSS program by minimizing the sums of difference squares and increasing the square of Pearson product correlation coefficient R2 of given data to predict the above aim.

The analysis strategy is done by Six-stages, which depend on:-the retaining percent of soil at different points on the GSD curve, increasing the points on the GSD curve in each step, and replacement between clay size fractions<0.002mm with clay size fractions< 0.005mm.

Test results shows that useful functional relationships exist between the compaction parameters from Particle-Size Analysis routine testing of soil.

It is concluded that the Square of Pearson product correlation coefficient R2 for correlation between the experimental and predicted values increases when increasing the number of grain sizes in the model. It also concluded that the OMC, and MDD can be predicting efficiently from grain size analysis only without depending on any other physical or chemical property of soil.

1.
Budhu
,
M.
“Soil mechanics and foundations” 3rd ed., ISBN 978-0-470-55684-9,
JOHN WILEY & SONS, INC
., Printed in the United States of America,
52
(
2010
).
2.
Đoković
Ksenija
,
Rakić
Dragoslav
, and
Ljubojev
Milenko
. “
Estimation compaction parameters of soil based on Atterberg limits
.”
Mining and Metallurgy Engineering Bor.
, Iss.
4
, pp.
1
16
(
2013
).
3.
Tesfamichael
Tsegaye
,
Dr. Henok
Fikre
, and
Tadesse
Abebe
. “
Correlation between compaction characteristics and Atterberg limits of fine-grained soil found in Addis Ababa
.”
International Journal of Scientific & Engineering Research
, Volume
8
, Issue
6
(
2017
).
4.
Maher
Omar
,
Abdallah Shanableh, Omer Mughieda, Mohamed Arab, and Waleed Zeiada, Rami Al-Ruzouqab. “Advanced mathematical models and their comparison to predict compaction properties of fine-grained soils from various physical properties
.”
Soils and Foundations Tokyo 58(6)
, Volume
58
, Issue
6
, pp
1383
1399
(
2018
).
5.
Dhurgham Abdul Jaleel Rasool
Al-Hamdani
,
Hana Mahmoud Amer
Al-Kasaar
, and
Hussain Ali Muhammad
Zani
. “
Prediction dry density of soil from some physical and chemical properties
.”
Global Journal of Research in Engineering, S.l.
, Vol
18
, No
2-E
(
2018
).
6.
Osman
Sivrikaya
,
Cafer
Kayadelen
and
Emre
Cecen
. “
Prediction of the compaction parameters for coarse-grained soils with fines content by MLR and GEP
.”
Acta geotechnica Slovenica
,
10
(
2
):
29
41
(
2013
).
7.
NG
K.S.
,
Chew
Y.M.
,
Osman
M.H.
, and
Mohamad Ghazali
S.K.
Estimating maximum dry density and optimum moisture content of compacted soils
.”
Conference: International Conference on Advances in Civil and Environmental Engineering
, (
2015
).
8.
Anjita
N. A.
,
Christy Antony
George
, and
Sowmya. V.
Krishnankutty
. “
Prediction of maximum dry density of Soil using genetic algorithm
.”
International Journal of Engineering Research & Technology
, Vol.
6
Issue
03
(
2017
).
9.
Charles M. O.
Nwaiwu1
, and
Ethelbert O.
Mezie
. “
Prediction of maximum dry unit weight and optimum moisture content for coarse-grained lateritic soils
.”
Soils and Rocks
,
44
(
1
):
1
10
(
2021
).
10.
A.K.
Shrivastava
, and
Dr. P. K.
Jain
. “
Prediction of compaction parameters using regression and ANN Tools
.”
International Journal for Scientific Research & Development
, Vol.
3
, Issue
11
(
2016
).
11.
Mahmoud Hassan
lourad
,
Alireza
Ardakani
,
Afshin
Kordnaeij
, and
Hossein Mola
Abasi
. “
Dry unit weight of compacted soils prediction using GMDH type neural network
European Physical Journal Plus
,
132
(
8
) Published: 21(
2017
).
This content is only available via PDF.
You do not currently have access to this content.