Evaluation of Creep Parameters in Geopolymers Derived From Mining Industry Waste
Bibliographic record
Abstract
Geopolymeric materials have recently been considered a revolutionary class of materials, due to the diversity of applications where they can be used, this derived from their good mechanical resistance, thermochemical stability and good fire resistance. From 1970 to the present, a notable increase has been observed in the number of scientific publications dealing with the synthesis and applications of geopolymeric materials with industrial application. Several publications have reported geopolymers as alternative materials to Portland cement, which will allow having a green construction industry in the coming years. The elaboration of geopolymeric materials is relatively simple, beginning with the identification of a source rich in amorphous aluminosilicates and a hardening compound (mainly alkaline in nature) which are properly mixed until a homogeneous and workable paste is obtained, then can harden at room temperature and acquire good mechanical resistance in a few hours. On the other hand, mining in Peru is one of the most important industries due to its contribution to the economic development of the country. However, mining is also one of the industries with the greatest negative impact on the environment, mainly due to the enormous amount of inorganic waste that it generates and which is currently accumulated without any prospect of recycling or reuse. Therefore, this research was conducted to elaborate concretes from mining tailings and to evaluate their mechanical behavior in creep (between 500 and 600 C). The results obtained allow us to propose the use of mining tailings for the elaboration of geopolymeric thermal barriers that work in conditions of intermediate temperatures, up to 600 C.
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 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 | 0.001 | 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".