Characterization of Clay Materials and Performance Evaluation of Fired Clay Composites Made for Low-Cost Housing
Bibliographic record
Abstract
This study deals with the physico-chemical, mineralogical and geotechnical characterization of alluvial clays from Batchenga in Cameroon with a view to their use as building materials for housing. The alluvial clay (Arg.All) was collected in the locality of Batchenga at the village Natchigal (4˚20'40''N and 11˚37'40''E at 378 m altitude) and was fired between 900˚C and 1100˚C. Characterization was performed by XRD, XRF, DTA/DTG, and firing tests. XRD, XRF, DTA/DTG infrared analysis methods were performed on these clays. The linear shrinkage, mechanical strengths, water absorption, porosity and density were measured on the fired products. The results obtained show that the major oxides are for the Arg.Lat SiO2 (72.13%), Al2O3 (14.1%), Fe2O3 (4.45%) and for the Arg.All: SiO2 (48.91%), Al2O3 (23.79%), Fe2O3 (9.54%). The fired products based on alluvial clay, present the flexural strength of 4.45 MPa at 900˚C and 6.80 MPa at 1100˚C. As for those based on lateritic clay, the flexural strength is 0.53 and 0.76 MPa respectively at 900 and 1100˚C. The porosity is 33.69% at 900˚C and 22.93% at 1100˚C for the alluvial clay and 39.55% at 900˚C and 36.01% for the lateritic clay at 1100˚C. Water absorption is 18% to 11.16% for alluvial clay and 22.43% to 21.16% for lateritic clay at 900˚C and 1100˚C respectively. These results suggest that alluvial clay and its firing products have better physico-chemical, geotechnical and mechanical characteristics regardless of the firing temperature of the manufactured products. The addition of degreaser is recommended to improve the mechanical performance of lateritic clay.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".