Field monitoring of MTQ 202ME concrete bridge barriers reinforced with GFRP and steel bars
Bibliographic record
Abstract
Through a collaboration project between the Ministry of Transportation of Quebec (MTQ) and the University of Sherbrooke, the field monitoring of GFRP- and steel-reinforced concrete barriers is being conducted. The barriers are constructed on a 6-lane bridge using high-performance concrete with a compressive strength of 50 MPa after 28 days. The barrier under consideration was median barrier type MTQ 202ME constructed on the 410-overpass bridge on the Highway 410 separating the 6-lane bridge into three lanes in each direction. The field monitoring included two sections of 23 m-long and 24 m-long. The first section (24 m-long) was reinforced with GFRP bars and the second one (23 m-long) was reinforced with galvanized steel bars. The main objective was to monitor the crack initiation and propagation as well as the strain evolution in both GFRP- and steel-reinforced sections. Besides, the effect of early age shrinkage and cracking of the high-performance concrete was captured thought the monitoring reading. The GFRP bars were instrumented with fiber-optic sensors (FOS) at different locations along the barrier length while the steel bars were instrumented with vibrating wire strain gauges (VWSG). Thermometers (TH-T) were also used for temperature measurements. In addition to the FOS and the vibrating wire strain gauges, electrical resistance strain gauges (ESG) were also used for additional measurements. The vibrating wire sensors and thermometers were connected to two multiplexers and a Datalogger type CR10X to capture their readings while the FOS sensors were connected to a 16-channel DMI unit to capture and store their readings. The readings of the ESG, however, were captured using the P-3500 readout unit. The results and the general discussion of the measured readings as well as some concluding remarks are presented.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".