Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Owing to the complex features of blasting vibration damage assessment systems, a gradient boosted machine (GBM) model is developed for the classification of residential structure damage (RSD) due to blasting vibrations of open pit mining. Twelve indicators are defined as the indices for the prediction of RSD in the proposed model. These are: peak particle velocity, dominant frequency, dominant frequency duration, distance, maximum safe charge per delay, compressive strength of mortar joints, ratio of brick area to house area, height of residential house, roof structures, beam-column frames, quality of construction, and site conditions. The GBM model is achieved by training 108 sets of measured data of blasting vibration. A 10-fold cross-validation procedure was applied to determine the optimal parameter values during modeling, and an external testing set was employed to validate the prediction performance of the model. Two performance measures – classification accuracy rate and Cohen’s kappa – have been employed. The analysis of accuracy together with kappa for the dataset demonstrate that the GBM model has high credibility as it achieves a comparable median classification accuracy rate and Cohen’s kappa values of 91.7% and 0.875 for the prediction of RSD, respectively.
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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 it