Development of CAD-Spreadsheet integrated solution to Optimazing Site Grading Design for industrial Construction
Bibliographic record
Abstract
Industrial construction covers a wide range of construction projects that are essential to our utilities and basic industries, such as petroleum refineries and petrochemical plants, synthetic fuel plants, fossil fuel and nuclear power plants etc. Land formation for an industrial construction site needs to consider many engineering constraints such as (1) ensuring proper drainage, (2) prevention of flood, (3) driving safety, (4) optimizing earthwork by balancing cut and fill, (5) minimizing truck travel distances in earthmoving, and (6) proper equipment matching for achieving high equipment utilization rates. We have developed a computer-based application framework by seamlessly integrating earthwork design in CAD and earthwork optimization in spreadsheet, in order to facilitate the practical application of the proposed framework on site formation for industrial construction. This paper presents an optimization problem formulation in an Excel spreadsheet model based on the least squares method. The Solver utility for optimization analysis in Excel provides a cost-effective tool to identify the optimum design surface model satisfying practical constraints on slopes and the highest elevation. A case study is used to demonstrate the effectiveness of the proposed application framework for earthwork optimization based on site formation on an industrial project in Alberta.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".