Modeling the Time-Dependent Variation of Road Salt Concentrations Using Analytical and Machine-Learning Approaches to Advance Service Life Predictions for Concrete Structures
Bibliographic record
Abstract
The exposure of concrete structures to environmental and climatic conditions is detrimental to their durability. In northern climates, the key contributor to their degradation is corrosion of the reinforcing steel because of chloride ions originating from de-icing salts applied on roadways during the winter season. In consequence, a key input parameter for predicting the time to the initiation of corrosion for concrete elements is the time history of the concentration of chloride ions at their surfaces. To investigate this issue, a specialized mobile monitoring station was deployed along a roadway over several winter seasons to collect data on salting operations, weather conditions, and the temporal variation of chloride ion levels on the roadway. At first, salting operations were monitored, and then exploratory and machine-learning algorithms were applied to develop relationships between weather conditions, road conditions, and chloride ion concentrations. The first proposed model is based on the simulation modeling approach, while the second is based on the machine-learning XGBoost model. The findings demonstrate that both models can predict the variation of salt concentration on the road surface as a function of time after a salting operation. By accounting for the time dependency of surface chloride in service life models, more accurate predictions of corrosion initiation time are possible, since the rate of penetration of chloride ions is highly dependent on wetting/drying cycles throughout the winter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".