The First Moving Load Simulator for Testing Bridges in Canada and its Application on a Full Scale Precast Box Girder
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Bibliographic record
Abstract
This thesis describes the development of a unique Moving Load Simulator (MLS) for testing bridge superstructure with a footprint up to 4x17 m, and its first application to test a full scale B900 prestressed concrete box girder. The aim of the experiment was to verify that the MLS can apply cyclic loading in a controlled laboratory environment, under realistic highway scale 'rolling wheel loads', determined through code procedures as well as through finite element analysis of a real bridge. The MLS has two half-axles of a large tandem, each comprising a dual 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 CHBDC. The maximum travel range and speed are 14.9 m and 6 ms, respectively. A bridge design of eight adjacent B900 girders of 27.6 m span was used as a case study. Load distribution analyses were conducted using both; a finite element model of the full bridge under various CL-625 truck loading configurations and the CHBDC load distribution method, and both agreed well. Load scaling analysis of the girder share was then conducted to account for shortening it to 16 m, resulting in two-115 kN MLS design loads, 1.2 m apart. This verifies that the MLS is indeed capable of applying full scale highway loads in a laboratory environment, which is novel. Multiple passes were conducted at various loads of 40-100% of design load and at speeds of 1-5 ms to examine the machine and girder behaviours. It was found that load fluctuates by less than 10% of full capacity and a 0.13 sec/cycle time lag occurs. The measured girder deflection and elastic strains were 11-20% lower than predicted theoretically. The estimated time to complete 3M cycles in approximately 4.5 months. With this machine now verified, future tests will involve fatigue tests for months at a time.
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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