Machine learning assessment of mechanical properties of oil palm shell concrete
Bibliographic record
Abstract
The escalating global demand for natural coarse aggregates in concrete production has intensified concerns over resource depletion and environmental degradation. As a sustainable alternative, oil palm shell (OPS) has emerged as a promising lightweight aggregate, yet its mechanical performance requires robust predictive modeling to facilitate wider adoption. This study develops and evaluates advanced data-driven models to predict the uniaxial compressive strength (UCS) of OPS concrete. An extensive experimental database of 377 specimens was compiled, incorporating key mix design variables including cement, water, sand, OPS content, superplasticizer dosage, and curing age. Multiple artificial neural network (ANN) architectures were systematically compared against conventional multi-linear regression (MLR). Results demonstrate that the ANN outperformed MLR by a wide margin, with the optimal two-hidden-layer architecture (6-13-3-1) achieving superior predictive accuracy (MAPE = 6.983%, R = 0.977). Sensitivity analysis further highlighted water, cement, and OPS content as the dominant parameters influencing UCS. Beyond establishing OPS as a viable eco-efficient aggregate, this work underscores the power of machine learning in optimizing mix design and accelerating the practical deployment of sustainable concretes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".