An expert system to optimize cost and schedule of heavy earthmoving operations for earth- and rock- filled dam projects
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
Success of major embankment dam construction projects is measured by the enormity of optimizing costs and schedules of selected heavy equipment based on their operational analyses. In this paper, the main objective is geared towards developing a knowledge-based decision support system for optimizing costs of heavy earthmoving operations and corresponding linear schedules at early design stages. Also, the proposed system is capable of generating an automated linear schedule based on stochastic scheduling techniques. Thus, a meta-heuristic simulated approach utilizing a metropolis algorithm is implemented to assist in generating optimized line-of balances. The successful implementation of the proposed system will provide the user with optimum fleet of equipment for performing earthwork operations and linear scheduling for strategic planning purposes. Towards the end, an actual dam construction project is utilized to numerically validate the proposed system and quantify its degree of accuracy. Results presented in this study are anticipated to be of major significance to owners, designers, and construction managers specialized in embankment dams heavy earthmoving operations and would contribute to the database of fleet management systems by incorporating a novel system that integrates heavy equipment economical operational analyses with its corresponding line of balance.
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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 | 0.001 | 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