Prediction of chloride ingress for palm kernel shell concrete
Bibliographic record
Abstract
Steel reinforcement corrosion emanating from ingress of chloride into concrete is the key reason for weakening of concrete structures globally.Infiltration of chloride into concrete happens by absorption and diffusion.On the other hand, reinforced concrete containing supplementary materials is more susceptible to corrosion exposure due to its high permeability.In this paper, two concentrations of sodium chloride (NaCl) were applied on concrete containing palm kernel shell as full replacement to granite.Concrete cube specimens of Grade 20 were cast into 150 mm by 150 mm by 150 mm moulds, and their workability were determined by compacting factor and slump tests.The hardened specimens were soaked in sodium chloride (NaCl) solution of 3% and 6% concentration.Spray, Absorption and Compressive strength tests were conducted at 7, 14, 21 and 28 days.Equations were generated by means of the data gotten from the laboratory tests to forecast the chloride penetration depth into the palm kernel shell concrete under the conditions considered in this work.The models generated revealed that absorption affected chloride ingress into the concrete significantly at 6% NaCl concentration.The models also reveal that the cover to reinforcements in Palm Kernel Shell Concrete subjected to chloride attack should be more than what is presently endorsed for reinforced concrete structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".