Designing a state‐of‐the‐art monitoring system in challenging operating conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
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 it