Designing a state‐of‐the‐art monitoring system in challenging operating conditions
Bibliographic record
Abstract
Abstract The construction joint venture NouvLR is constructing the new light rail network in Montreal. One of the major challenges is the advance below the existing runways and taxiways of the Montreal‐Trudeau International airport and the construction of the subway station below the airport. The airport must remain under operation during the construction, and very tight requirements have been imposed regarding the tolerable surface settlements and availability of the monitoring data. The regulations regarding operations of the airport and possible presence of foreign objects in vicinity of its runways and taxiways represent an additional challenge, requiring usage of less straightforward monitoring concepts. The works for the installation of the monitoring equipment must be closely coordinated with airport operations and require a reliable schedule. The final geotechnical monitoring design has been performed in close cooperation with the contractor and in tight coordination with the airport authority. In order to allow more straightforward communication, 3D modelling and BIM‐methodology have been used to clearly represent the monitoring equipment, as well as the works required for their installation. The line‐of‐sight considerations in case of tachymeter measurements have been thus directly incorporated and dealt with. The monitoring design has to fulfil the aims of real‐time monitoring of the system behaviour during the construction and the long‐term monitoring of the newly built tunnel, demonstrating the compliance of the structure with the design. The paper is concluded by a summary of ”lessons learned“ regarding the issues of adverse accessibility and a challenging, high risk general environment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".