Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars
Bibliographic record
Abstract
Abstract This research uses Artificial Neural Network (ANN) as a soft computing technique to predict the carbonation depth and service life of cementitious materials with low clinker content. For this purpose, different mortars were prepared with 0, 10, 15, 20, 25 and 30% replacement levels of cement by metakaolin (MK), brick powder (BP) and calcined sediments (CS). The experimental results of the carbonation depth were obtained under natural and accelerated carbonation conditions for exposure periods of 12 months and 28 days respectively. ANN was utilized taking into account the main influential factors on mortars carbonation, including mix proportions and environmental conditions. For the ANN model, seven datasets were considered as inputs, covering mineral admixture content, cement content, curing time, CO 2 concentration, relative humidity, temperature and CO 2 exposure time, in addition to one output parameter which is the carbonation depth. The results show that the resistance to carbonation of the mortars decreases with the increase of cement substitution by MK, BP or CS. The network model gives good performance values in the validation and testing set with a lower mean square error (MSE) and a higher determination coefficient ( R ). The predicted carbonation depths are in good agreement with the experimental measurements of carbonation depths, confirming the efficiency of the developed ANN model to be applied to correctly estimate the carbonation depth of cementitious materials with low clinker content.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".