Soil characterization, CBR modeling, and spatial variability analysis for road subgrade: a case study of Danchuwa – Jajere Road, Yobe State, Nigeria
Bibliographic record
Abstract
Abstract Road construction projects require a thorough understanding of soil properties to ensure the stability and longevity of the infrastructure. This study investigates soil properties along a proposed 34 km road alignment in Yobe State, Nigeria, to characterize soil variability for road construction and develop a predictive model for California Bearing Ratio (CBR). Of the 34 soil samples analyzed, 30 were classified as A-3(1) and four as A-1(1) according to the AASHTO system. Geotechnical testing, including particle size distribution (grading percentages: gravel 0.02%–75.34%, sand 15.5%–90.88%, fines 8.92%–34.84%), Atterberg limits (liquid limits 17%–33%, plastic limits 14%–27%, plasticity index <12%), specific gravity (2.01 to 2.73), compaction (maximum dry density 1.83–2.19 Mg m−3, optimum moisture content 7.29%–14.42%), and CBR tests (values ranging from 5%–62%), were conducted. Correlation analyses revealed strong positive relationships between maximum dry density (r = 0.82) and specific gravity (r = 0.89) with CBR values. Cluster analysis segmented the samples into four distinct groups: Cluster 0 (11 samples), Cluster 1 (9 samples), Cluster 2 (5 samples), and Cluster 3 (9 samples). A linear regression model predicted CBR using maximum dry density and specific gravity (mean squared error = 9.82, R2 = 0.92). Based on CBR criteria, 8 out of 34 samples (CBR 20%–53%) satisfied subbase requirements, while none met the recommended minimum CBR of 80% for base course materials. This study enhances road construction planning through soil variability analysis, effective soil categorization via cluster analysis, and a reliable CBR prediction model. While on-site materials are unsuitable for subgrade and subbase layers, alternative materials or ground improvement techniques are recommended for the base course layer to enhance bearing capacity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".