Bibliographic record
Abstract
The construction of highway projects is often associated with traffic congestion due to partial or complete road closures during construction operations. In order to minimize these adverse effects on the travelling public, state highway agencies across North America are attempting to minimize the duration of construction by providing contractors with financial incentives to reduce construction duration. In order to realize these financial benefits, a highway contractor needs to establish a delicate balance between the financial rewards realized from reducing construction duration and the additional direct cost that may result from such acceleration. This paper presents a computer model for scheduling of highway construction that is capable of establishing this optimum balance. The development of the model is based on an object-oriented modeling approach and consists of three stages: analysis, design, and implementation. The model incorporates two recently developed algorithms for resource-driven scheduling and optimized scheduling. The model is implemented using C++ programming language as a 32-bit windows application that runs on Microsoft Windows 98 and NT and provides a user-friendly interface. The developed model can assist highway contractors in formulating an optimum resource utilization strategy for minimizing the overall costs of highway construction. This can prove useful to contractors and can assist them in meeting the demands of various state highway agencies to reduce the duration of construction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".