Strength development of cemented tailings materials containing polycarboxylate ether-based superplasticizer: experimental results on the effect of time and temperature
Bibliographic record
Abstract
Superplasticizers are widely used in the backfill industry for the improvement of the flow ability of cemented paste backfill (CPB) while keeping water content low. However, the coupled effects of temperature and curing time on the mechanical strength of CPB with superplasticizers are poorly understood. This paper presents new findings of research conducted to experimentally assess the effects of a polycarboxylate-based superplasticizer on the strength of CPB subjected to varying curing time (1, 3, 7, and 28 days) and temperature (2 °C, 20 °C, and 35 °C). The binders used were Portland cement type I, fly ash, and blast furnace slag. Superplasticizer contents of 0, 0.125, and 0.25 percent of the total weight of the CPB were added. Various microstructural analyses and monitoring programs were also conducted to understand the principles behind the patterns in the strength of different CPB samples. The results obtained show that the unconfined compressive strength (UCS) of the CPB containing polycarboxylate-based superplasticizer increases with time. Moreover, the increase in superplasticizer content was observed to improve the UCS of the CPB (0.25% > 0.125% > 0%). The temperature was also observed to play an important role in strength development as the UCS increases with the rise in the curing temperature for all samples. It is also found that the temperature-induced strength increase is more significant for the CPBs that contain the superplasticizer than for those without superplasticizer. It was also observed that the CPB containing superplasticizer maintained a relatively similar strength upon replacing the cement with 50% slag while showing a sharp decline with fly ash. The findings from this study will be useful towards a cost-effective design of backfill structures.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".