Strengthening of a Bridge Pier with HPC: Modeling of Restrained Shrinkage Cracking
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
After more than fifty years from the opening of the largely discussed “Autostrada del Sole” Highway in 1964, the infrastructure system in Italy appears marked by the passing of time, similarly to what observed in several other countries worldwide. The great heterogeneity of the Italian landscape has determined a great variety of construction types, such as large span concrete bridges over the northern rivers and large arch concrete bridges over the valleys of the central region. Increment of vehicle traffic and new seismic regulations are setting new requirements to adapt the existing infrastructure, which should be otherwise replaced. Moreover, reinforced concrete (RC) aging and deterioration have led to structural and material degradation, including severe cracking and corrosion. Specialized materials such as High Performance Concrete (HPC) could represent a viable convenient solution for repairing, strengthening and retrofitting of RC structures as both structural capacity and durability can be refurbished. However, alongside high mechanical performance, HPC is characterized by a high cracking sensitivity at very early age, due to its high stiffness and shrinkage. Restrained shrinkage cracking, particularly significant in repaired structures where the existing concrete generates a considerable restraint against the free movement of the repair material, may represent a limit to the effective application of these materials. For this reason, shrinkage compatibility of HPC with the existing concrete substrate needs to be experimentally and numerically assessed. A study is herein presented where, based on experimental tests, different numerical models are developed and compared to assess and eventually minimize the risk of shrinkage cracking in bridge piers strengthened with HPC.
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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