PRODUCTIVITY IMPROVEMENT IN OPERATING AUTONOMOUS PLANTS SUBJECT TO RANDOM BREAKDOWNS IN CONSTRUCTION
Why this work is in the frame
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Bibliographic record
Abstract
Realizing continuous operations of autonomous plants subject to finite specialist crew resources for maintenance and repair is vital to achieving productivity and cost-effectiveness in construction operations. This paper presents a practical Monte Carlo simulation-based method to develop autonomous plants operations and maintenance programs. To balance the cost of plant production loss against the cost of hiring maintenance crews, we define a cost function which factors in production output value, resource utilization efficiency and direct cost in connection with both autonomous plants and maintenance crews. An illustration case of planning maintenance crew resources in operating autonomous crushing plants at a quarry site is used to shed light on required input data, simulation processing, and output analysis. The case also has increasing relevance to the construction industry in the near future in terms of planning the operation of a fleet of autonomous equipment in site operations.
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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