A decision approach on risk-control scheme recognition for karst excavation engineering
Bibliographic record
Abstract
Risk events can be frequently encountered in karst geological environments during excavation construction; thus, a suitable risk-control scheme is essential to reduce the negative impact of accidents. A decision-making approach based on the fuzzy VIKOR method is proposed to identify the optimal risk-control scheme, where a triangular fuzzy set is adopted to express the experts’ judgements. A decision hierarchy is constructed based on four criteria and 12 sub-criteria, which are determined based on engineering experience and the construction environment. The developed approach was utilized to determine the optimal risk-control scheme to address risk events for excavation construction in karst regions. A countermeasure scheme, that is, pressure grouting with a two-phase liquid system, was implemented. The basic principles and steps of the implementation of the proposed scheme are presented. The applicability of the identified scheme was verified by the core recovery and modified number of blows using a standard penetration test. A flowchart of optimal scheme identification for geotechnical engineering practice is provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".