Bibliographic record
Abstract
The purpose of this paper is to present the application of the Building Information Modeling (BIM) methodology in the rehabilitation of a 100-year-old tunnel for the Réseau Express Métropolitain (REM) project in Montréal, Canada. Rehabilitation consisted of repairing the interior structure, adding a fire-rated wall to comply with modern standards, optimizing the track alignment, and redesigning the drainage, ventilation and electrical systems. Niches were excavated in the side walls of the tunnel for telecommunication needs and local enlargement of the roof was required for the installation of jet fans. Due to the complexity of the design, developing an accurate representation of the structure was very important. 3D laser scanning played an important role in providing measurements of the interior structure and current condition. Grasshopper and Rhino scripts were also incorporated to the workflow to accurately model the repetitive structural elements along the tunnel to match with survey points, elevations and required clearances. Extensive clash detection analysis was performed in Navisworks to identify clashes between the new train envelopes and the existing tunnel wall, as well as between the train envelopes and new center wall walkway. The intent of this exercise was to accurately identify clash zones and identify areas along the tunnel where additional excavation in the existing side walls would be optimized so that both the train can safely pass through the tunnel and the excavation is cost-efficient. The paper also describes the challenges and limitations that were identified, mostly related to the interoperability, exporting processes, processing times due to the large size of the files, and software glitches. Overall, the creation of a dynamic model was significantly beneficial, especially during the construction phase, as it was updated to reflect the actual conditions, thus giving the opportunity to the designers to further optimize the design as needed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".