Chloride ingress in de-icing salt-exposed bridge: Numerical modeling and field investigations
Bibliographic record
Abstract
This research focuses on assessing the durability of various components of an onshore section of the Original Champlain Bridge in Montreal, specifically in relation to chloride ingress from de-icing salting operations. Chloride penetration in both the original and repaired concrete are both numerically modelled and measured. The transport characteristics of repaired and original parts of the concrete were determined using non-destructive air permeability tests and used in the TransChlor® to predict chloride ingress into the air-exposed repaired sections and underlying unrepaired sections over the service life of the bridge. By selecting appropriate boundary conditions, and historical climatic data, the predicted chloride profiles are shown to be a close match to core sample data from both the repaired and unrepaired sections of structural elements. This research proposes a blind a-priori prediction method for analyzing and predicting chloride ingress in ageing concrete structures as opposed to a-posteriori predictions made by matching chloride profiles obtained from cores, which is a common practice in the literature. Furthermore, it was clearly demonstrated that cathodic protection is effective in preventing chloride ingress into concrete. However, the ability to predict chloride ingress in the presence of cathodic protection was identified as a gap in current chloride ingress models such as TransChlor®, and an objective for future research.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".