The first rolling load simulator (ROLLS) for testing bridges in Canada and its application on a full-scale precast box girder
Bibliographic record
Abstract
This paper describes the development of a unique rolling load simulator (ROLLS) for testing bridge superstructure with a footprint up to 4 m ×17 m, and its first application to test a full-scale 1220 mm ×900 mm ×16000 mm B900 prestressed concrete box girder. This facility at Queen’s University in Kingston, Ontario, is the first of its kind in Canada. ROLLS can apply cyclic loading in a controlled laboratory environment, under realistic highway scale ‘rolling wheel loads’, in lieu of the conventional ‘pulsating stationary loads’. It has two half-axles of a large tandem, each comprising a dual 1140 mm diameter air-inflated tires spaced at either 1.2 or 2.4 m. Each half-axle can apply up to 125 kN, representing the heaviest half-axle load of the CL-625 design truck of the Canadian Highway Bridge Design Code (CHBDC). The maximum travel range and speed are 14.9 m and 6 m/s, respectively. A case study involving analysis of a bridge with eight adjacent B900 box girders of 27.6 m span was carried out prior to experimentally testing one of the girders using ROLLS. Load distribution analyses were conducted using both (i) a finite element model of the full bridge under various CL-625 truck loading configurations and (ii) the CHBDC load distribution method, and both agreed well. Scaling analysis of the girder load share was then conducted to account for shortening it to 16 m to fit in the laboratory, resulting in two-115 kN ROLLS design loads, 1.2 m apart. Multiple passes were conducted at various loads of 40%–100% of the design load, at speeds of 1–5 m/s to examine the machine and girder behaviours. It was found that the applied load fluctuates by less than 10% of full capacity and a 0.13 s/cycle time lag occurs. The measured girder deflection and elastic strains were 11%–20% lower than predicted theoretically. With the two half-axles assembly spaced at 1.2 m, the apparatus has the ability to complete three million cycles in approximately 4.5 months if ran continuously at 5 m/s.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".