Development of stabilised soils for construction applications
Bibliographic record
Abstract
Lateritic soils from Papua New Guinea were stabilised with various percentages of rice husk ash (RHA), finely ground natural lime, cement and their combinations. The influence of stabilisers and their combinations was evaluated through Atterberg limits, standard Proctor compaction, unconfined compressive strength, splitting tensile strength, modulus of elasticity and California bearing ratio (CBR) tests. The durability of 38 stabilised soil mixtures was also investigated by studying the influence of water immersion on strength, water sorptivity and drying shrinkage. Correlations between compressive strength, modulus of elasticity and CBR were also established. Theoretical analysis of pavements incorporating subgrades improved by stabilised lateritic soils under traffic loads showed technical benefits in comparison with conventional flexible pavements without improved subgrades. Suitable stabilised soil mixtures using RHA, lime, cement and their combinations which can be used for the construction of road pavements, airfields, earth dams and low-cost housing are proposed. The use of locally available soils, RHA and lime in the production of stabilised soils for such applications can provide sustainability for the local construction industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".