Artificial neural network modeling of self-compacting concrete mixed and cured with seawater for compressive strength and chloride penetration prediction
Bibliographic record
Abstract
The large-scale use of concrete requires reliable quality assessment to ensure workability, mechanical properties, and durability. Conventional testing methods are often costly and time-consuming. This study explores predictive modeling as an efficient alternative, focusing on self-compacting concrete (SCC) produced and cured with seawater, silica fume, and fly ash. Workability indicators, including slump flow, J-ring, visual stability index (VSI), and air content, were used to predict compressive strength and chloride concentration. Artificial neural networks (ANNs) and Classification and Regression Trees (CART) were applied. The ANN models achieved high accuracy, with compressive strength predicted at a minimum mean squared error (MSE) of 0.085638. The chloride content prediction achieved an R² of 0.9429. CART analysis revealed that air content was the most significant factor influencing compressive strength, while the J-ring had the strongest impact on chloride content. A comparative study demonstrated that ANNs outperformed random forest regression in predictive capability. These results highlight the value of machine learning in concrete research, offering a cost-effective and time-saving method for property evaluation. The findings also support the sustainable use of seawater and supplementary cementitious materials in the production of concrete. The novelty of this study lies in predicting the compressive strength and chloride ion concentration of self-compacting concrete produced and cured with seawater and pozzolans. Neural networks and machine learning were applied for this prediction, an approach not previously explored.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".