Prediction of blast-induced ground vibration using multi-variate regression analysis in an opencast mine
Bibliographic record
Abstract
The consumption of hydrocarbon is increasing day by day. A number of technologies are being used to meet out the demanded quality. The drilling and blasting is a cheapest way to exploration and excavation in mining industries. The blasting creates excessive amount of energy in different form of ground vibration as shaking of Earth, flyrock, removal and transportation of overburden rockmass and other noise. Blast-induced ground vibration has some adverse effects on surrounding environment as well as community living nearby the opencast mine. The study was conducted at opencast coal mine in Chhattisgarh. A total number of 32 data sets have been measured with different parameters such as; maximum charge per delay (MCPD), observation distance, charge length, spacing, burden, blast hole depth, hole diameter, etc. as well as peak particle velocity (PPV), frequency and peak vector sum (PVS). In present study, main focus on measurement and prediction of peak particle velocity by different predictor model (USBM, Indian Standard, DGMS) and multi-variate statistical regression analysis (MVSRA). Simple linear regression model (SLRM) is used to determine the site characteristics constants. The constants are used to establish new prediction model equations among different parameters. Finally, assess the blast induced ground vibration on the basis of measured and predicted peak particle velocity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".