Monitoring During Lateral Bridge Slide
Bibliographic record
Abstract
Slide-in bridge construction (SIBC) is an accelerated bridge construction method. In U.S. state of the art and state of the practice today, the bridge superstructure is moved by sliding laterally into the final alignment following a sequence of construction and demolition events. SIBC implementation components require a temporary support structure, a slide system with railing girders and polytetrafluoroethylene pads or rollers, and an actuating system to initiate and maintain the slide movement. The M-100 bridge over the Canadian National (CN) railway was the third SIBC project implemented by the Michigan Department of Transportation. Each SIBC implementation has been so far unique because the unknowns include slide properties contributing to friction between surfaces, pushing and pulling force levels, and monitoring and controlling the force levels. The purpose of standardization is to develop repeatable procedures for the SIBC method. One aspect of standardization is to develop an understanding of the structural response and the forces developed in the system during slide activities. This understanding requires documentation of various SIBC practices, simulation of slide activities, and monitoring the structural response. The activities of the M-100 road over the CN railway bridge slide, instrumentation and monitoring of the structural response, and the use of acquired acceleration data to calculate the forces that developed during the slide activities are presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".