Behavior of Cementitious Materials under the Effect of an Eco-Cement Based on Dredged Sludge
Bibliographic record
Abstract
This paper attempts to simulate the use of green materials from the silt in a dam, and reduce the harmful impacts of siltation on Algerian dams affected by frequent droughts and irregular rainfalls, which are resulted from climate change. These harsh weather conditions are the main cause of water erosion in Algeria, leading to a high silting level in many dams across the country. Therefore, it is necessary to dredge the considerable volumes of sludge in the dam areas. This paper treats the sludge dredged from the K’sob dam, and adds the treated sludge into cement, creating a hybrid binder that can be used in composition of cementitious materials. Specifically, the sludge extracted from the K’sob dam was characterized chemically, physically, mineralogically, and mechanically, and introduced both as a substitute of cement and a component in the mixture of ordinary concrete/mortar. The sludge was firstly activated through calcination, and added to cement at the mass dosages of 10%, 15%, and 20% separately. The mechanical behavior, especially that under compression, of cementitious materials (concrete/mortar) based on the treated sludge was studied through lab tests. The test results show that this technical innovation gives the finished product three major properties, namely, high strength, economy, and a beneficial ecological impact. The results obtained are encouraging and promise an optimal exploitation of the sludge from similar dam areas.
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 | 0.000 | 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) | 0.001 | 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".