Geographic information system based roadway construction planning
Bibliographic record
Abstract
The planning process for roadway construction involves a large amount of information on design, construction methods, quantities, unit costs, production rates, and site conditions. Therefore, it is very important to acquire, manage, and process the necessary information efficiently to produce a rigorous construction plan. The geographic information system (GIS) is a very effective tool for integrating and managing various types of information, including spatial and nonspatial data, required for roadway construction planning. This paper proposes a GIS-based system for improving roadway construction planning with its "interactive space scheduling" and "operation level planning" functions, which are supported by the integration of various data required for planning. The proposed system can assist construction planners in a unique way by integrating design and construction information and creating modularized design elements for space scheduling in real time using its interactive space scheduling function. In addition, operation level planning on earthwork can be conducted via the ability of the system to analyze haul routes in three dimensions and selecting the best equipment combination. It is expected that the proposed system could improve the efficiency of roadway construction planning.Key words: geographic information system (GIS), roadway construction planning, information integration, space scheduling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.007 | 0.001 |
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".