Inclusion of Ground Penetrating Radar within traditional deck testing and life cycle analysis
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
Traditional deck testing techniques (half-cell, chloride sampling, and reinforcement cover readings), provide useful information but can be destructive (chloride testing), and time consuming in the field. Unlike the more traditional deck testing techniques, Ground Penetrating Radar (GPR) collects more data in a much shorter period while giving comparable results. With proper data analysis to account for variable reinforcement cover and asphalt thickness, the information that GPR provides can also help determine what stage of service life the bridge is at. Once the stage of service life and remaining service life are known, rehabilitation strategies can be developed. These rehabilitation strategies then undergo a life cycle analysis via a Monte Carlo simulation to account for uncertainty. Finally, the rehabilitation options may be put through a decision matrix if non-financial factors, such as interference to the public, need to be considered in the evaluation of the final decision. This paper gives details of GPR studies conducted on three projects in Canada.
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