Construction Simulation and Environmental Impact Analysis: Towards a 4D-Based Analysis of Road Project Variants
Bibliographic record
Abstract
Road construction work has a multitude of impacts on its host environment, and the effect of these impacts varies according to the areas it crosses. Taking these impacts into account from the earliest stages of project planning is the ideal approach pursued by planners to ensure that their plans not only take these impacts into account but also mitigate their effects as much as possible. Drawing up a project schedule that considers the impact of the work requires an in-depth understanding of its scale, spatial extent, and timing. In practice, however, such an understanding is difficult to achieve due to the complex and variable nature of impacts. To help project planners understand the impacts of a road project from the outset so they can better plan mitigation measures, we have developed a conceptual framework for four-dimensional Building Information Modeling (4D BIM) deployment that visualizes the most significant impacts on the project site’s surrounding environment in terms of their spatial extent and progression over time. By testing the method on a case study of a road improvement project in northwestern Quebec, the method shows that, compared with traditional 2D methods, the proposed 3D and 4D impact visualization modeling provides an integral perspective for visualizing and understanding spatial changes in project impacts over time and enables different possible project implementation variants to be evaluated with relative ease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".