Multi-objective optimization of cement-based systems containing marine dredged sediment
Bibliographic record
Abstract
This study presents the integrated use of particle packing methodology and response surface methodology as an innovative mixture design for developing eco-friendly cement-based systems containing marine dredged sediment. The objective was to reduce the sand and cement contents of mortar mixtures for island applications, while maintaining the same properties as a reference mixture. The study examined three input variables for mixture design and optimization: sediment-to-total sand ratio ranging from 0.1 to 0.4, water-to-binder ratio ranging from 0.4 to 0.5, and cement paste content ranging from 0.35 to 0.45. As a result, three mixtures were developed based on three optimization objectives: (1) maintaining the same fresh and hardened properties as the reference (110 mm spread flow and 49.5 MPa strength); (2) maximizing the reduction of cement and sand contents with the use of a superplasticizer; and (3) maximizing the durability (as measured with the bulk electrical resistivity). The optimal mixture proportions showed reduced sand and cement contents up to 38 % and 15 %, respectively, with mechanical properties comparable to that of the reference. Moreover, capillary absorption and drying shrinkage of the optimized mixtures were reduced compared to the reference by up to 36 % and 16.5 %, respectively. It was evidenced that the combined use of mixture design methods can significantly contribute to the development of cementitious systems with balanced eco-efficiency and properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".