Data acquisition and analysis for highway construction using geographic information systems
Bibliographic record
Abstract
Earthmoving operations represent a sizeable percentage of highway construction costs. Accurate estimation of cut and fill quantities, as well as haul distances, are thus essential for developing realistic schedules and reliable cost estimates for highway construction projects. This paper presents a model designed to aid highway construction personnel in optimizing earthmoving operations by developing realistic mass haul diagrams. The model automates data acquisition and accounts for the presence of different soil strata and natural and (or) man-made obstructions affecting earthmoving plans. Geographic information systems (GIS) are employed to generate three-dimensional digital terrain models of the topography and soil profiles. The model is supported by a relational database for soil data and has been implemented in a prototype software developed in ArcView® environment. It provides a user-friendly interface to facilitate data entry and efficient reporting capabilities. The model has flexible input and output formats designed to facilitate data sharing with a number of commercially available software systems. A numerical example is presented to demonstrate the features of the developed model.Key words: data acquisition, geographic information systems, quantity estimating, earthwork optimization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".