Development and performance evaluation of a method to determine the maximum dry density of lateritic soils using specific gravity

Determining the Maximum Dry Density (MDD) of soil in a laboratory is often time-consuming and costly. This study aimed to develop a correlation that can be used to estimate the MDD of lateritic soils using Specific Gravity (Gs). Fourteen lateritic soil samples sourced from Uganda, classified as clayey sand and clayey gravel, were compacted using the Modified Proctor test according to BS1377:4-1990, and their specific gravities were determined using standard pycnometer method to BS1377:2-1990 standards. A linear regression analysis in Microsoft Excel revealed a positive correlation between MDD and Gs, with a coefficient of determination (R²) of 0.5459. The predictive accuracy of the correlation was determined using five additional samples, whereby an accuracy of 93% was achieved when comparing the MDD from the Proctor test to the MDD from the correlation. When data sourced from other researchers was incorporated in the process of performance evaluation, the accuracy of prediction dropped to 81%. The study concludes that MDD for clayey sand and clayey gravel of lateritic nature can be estimated using the formula: MDD = 0.9343Gs - 0.6494. In Uganda, this method is applicable for subgrade materials G3 and G7, which require a minimum relative field compaction of 90% and 93%, respectively, according to Uganda's Ministry of Works and Transport specifications.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-08-14
DOI
https://doi.org/10.1051/e3sconf/202564202019/pdf
Primary Topic
Soil and Unsaturated Flow
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and performance evaluation of a method to determine the maximum dry density of lateritic soils using specific gravity

Richard Kizza, Anita Atukunda, Gasper Abima
Springer Link (Chiba Institute of Technology)
Soil and Unsaturated Flow
article

Development and performance evaluation of a method to determine the maximum dry density of lateritic soils using specific gravity

Richard Kizza, Anita Atukunda, Gasper Abima
article en

Abstract

Determining the Maximum Dry Density (MDD) of soil in a laboratory is often time-consuming and costly. This study aimed to develop a correlation that can be used to estimate the MDD of lateritic soils using Specific Gravity (Gs). Fourteen lateritic soil samples sourced from Uganda, classified as clayey sand and clayey gravel, were compacted using the Modified Proctor test according to BS1377:4-1990, and their specific gravities were determined using standard pycnometer method to BS1377:2-1990 standards. A linear regression analysis in Microsoft Excel revealed a positive correlation between MDD and Gs, with a coefficient of determination (R²) of 0.5459. The predictive accuracy of the correlation was determined using five additional samples, whereby an accuracy of 93% was achieved when comparing the MDD from the Proctor test to the MDD from the correlation. When data sourced from other researchers was incorporated in the process of performance evaluation, the accuracy of prediction dropped to 81%. The study concludes that MDD for clayey sand and clayey gravel of lateritic nature can be estimated using the formula: MDD = 0.9343Gs - 0.6494. In Uganda, this method is applicable for subgrade materials G3 and G7, which require a minimum relative field compaction of 90% and 93%, respectively, according to Uganda's Ministry of Works and Transport specifications.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 32%
Soil and Unsaturated Flow
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.