Bibliographic record
Abstract
Obtaining durability in concrete structures over a long service life in chloride exposures requires knowledge of the concrete properties, relevant transport processes, depths of cover as well as minimization of cracking and construction defects. For example, imperfect curing can result in depth-dependent effects of the concrete cover’s resistance to chloride ingress. Several service life models with various levels of sophistication exist for prediction of time-to-corrosion of concrete structures exposed to chlorides. The model inputs have uncertainty associated with them such as boundary conditions (level of saturation and temperature), cover depths, diffusion coefficients, time-dependent changes, and rates of buildup of chlorides at the surface. The performance test methods used to obtain predictive model inputs as well as how models handle these properties have a dramatic impact on predicted service lives. Very few models deal with the influence of cracks or the fact that concrete in the cover zone will almost certainly have a higher diffusion coefficient than the bulk concrete as the result of imperfect curing or compaction. While many models account for variability in input properties, they will never be able to account for extremes in construction defects. Therefore, to ensure the reliability of service life predictions and to attain a concrete structure that achieves its predicted potential, designers, contractors and suppliers need to work together, using proper inspection, to ensure proper detailing, minimize defects, and adopt adequate, yet achievable, curing procedures. As well, concrete structures are often exposed to other destructive elements in addition to chlorides (eg. freezing or ASR) and this adds another level of complexity since regardless of cause, cracks will accelerate the ingress of chlorides. These issues are discussed along with the need to use performance-based specifications together with predictive models.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".