Chloride threshold level determination: Call for test standardization to limit variations in experimental methodology and resolve inherent experimental and modelling detection challenges
Bibliographic record
Abstract
Chloride-induced corrosion significantly threatens the durability of reinforced concrete structures, leading to deterioration, costly repairs, and potential structural failures. Accurately determining the steel reinforcing bar (rebar) chloride threshold level (CTL) is crucial for predicting corrosion onset, optimizing material selection, and estimating the service life of these structures. An ensemble machine learning model was trained using literature CTL data. Despite achieving a mean absolute error of 0.218 % weight of binder, a root mean square error of 0.321 % weight of binder, and an R² value of 0.751 on unseen data, the model's performance reveals limitations due to the wide variability in reported CTL, stemming from disparities in experimental methodologies including set-up and corrosion detection techniques. After model development, this paper also investigates challenges associated with CTL evaluation by comparing literature practices and providing insights to enhance data reliability and comparability. Factors impacting CTL evaluation includes corrosion detection techniques, initiation criteria, chloride introduction methods, testing setup, exposure solution compositions, chloride concentration measurement techniques, and rebar concrete/mortar cover thickness. This paper focused on the largely ignored aspect of these factors, some of which are inherent and nearly non-circumventable, and will continue to lead to suboptimal performance of any CTL predictive model when not addressed. Recommendations for standardizing practices are proposed to improve CTL assessment consistency, reliability of developed CTL predictive models, and accuracy of service life modeling.
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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 | 0.055 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".