Fresh, mechanical and durability properties of sustainable cement grouts incorporating dredged materials from UAE dams
Bibliographic record
Abstract
The paper proposes the use of dam dredged materials (DDM) in the construction industry in the United Arab Emirates (UAE). The used DDM in this study were collected from a prominent dam in UAE. Four cement grout mixes were prepared by replacing aggregates with the following percentages of DDM: 0%, 3%, 5% and 10%. The fresh, mechanical and durability performance of the grout mixes were examined. The slump and rheology of the prepared grout mixes were measured over two hours, with one-hour intervals between two successive measurements. The rheology was assessed by conducting shear flow and stress growth tests. Further, air content, fresh bulk density and initial setting time were measured for all mixes. The mechanical properties were evaluated through measuring the compressive strength and hardened density at 7, 28, 56 and 90 days. Additionally, shrinkage, electrical conductivity and electrical resistivity were measured to assess the durability of the prepared mixes. The results showed that the dam dredged materials enhanced the mechanical and durability of the tested grouts. For example, the 28-day strength increased by approximately 40% when DDM content was increased from 0% to 10%. Further, the DDM offset the shrinkage of the grouts, and shrinkage decreased with higher content of DDM. On the other hand, the cement grouts incorporating DDM were rated as medium resistance to chloride penetration after 90 days of curing. The cement grout mixes were implemented in two construction applications: tile adhesive and plaster. The samples for both applications showed favourable behaviour with no observed cracks after 11 months of exposure to harsh environmental conditions. The promising results of this study would contribute towards reducing the environmental impacts of dam dredged materials and preserving natural resources, leading to sustainable and economic benefits to the UAE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".