Multiphysical testing of strength development of cemented paste backfill containing superplasticizer
Bibliographic record
Abstract
Current laboratory procedures for curing and testing the mechanical strength of cemented paste backfill (CPB) do not take into account the complex Multiphysics (thermal, T; hydraulic, H; mechanical, M; chemical, C) processes that CPB structures are subjected to in the field. This oversight can lead to unreliable measurements and unsafe designs. In this study, a multiphysical curing and testing procedures for CPB with superplasticizer (CPB-PES) has been developed to evaluate its strength development under THMC curing conditions close to those encountered in the field. The obtained results demonstrated that the strength development of CPB-PES is greatly affected by the THMC factors and their interactions. The contributions of each one of the investigated THMC factors on the strength are not equally similar and greatly depend on the interaction between these factors and curing time. CPB samples with 0.125 % PES that underwent drainage during THMC curing showed a strength increase of up to 809 % after 28 days of curing, compared to the control samples. The strength of CPB-PES samples cured under THMC can be up to 57 % higher than that of samples cured under THC conditions. The results indicate a significant interaction between thermal (T; elevated field curing temperature) and chemical (C; superplasticizer and cement hydration) factors, between chemical (C) and mechanical (M; field curing stress) factors, as well as between thermal and mechanical factors. The influence of the mechanical factor on strength development was observed to be less pronounced compared to the impact of chemical and thermal factor, and is reduced at elevated curing temperatures. The findings underscore the critical importance of accounting for field-relevant THMC factors and their interactions in the determination of the CPB-PES strength development, which is essential for the design of safer and more economical CPB structures, ultimately enhancing mine productivity and safety.
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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".