Optimization of service life design of concrete infrastructures in corrosive environments under a changing climate
Bibliographic record
Abstract
The risk of failure of concrete infrastructures built in corrosive environments is increasing due to use of deicing salts, increased loads, inadequate maintenance and increased rate of deterioration due to climate change. Climate change leads to an increase in temperatures, which in turn leads to an increase in chloride diffusivity and rate of corrosion that yield an increase in probability of corrosion of reinforcing steel, concrete damage and a shortening of service life of concrete structures. The impact of temperature rise due to climate change on diffusivity is modeled using the Arrhenius relationship. Uncertainties in the parameters governing the service life, such as concrete cover depth, chloride threshold, chloride diffusion coefficient, surface chloride content are considered by modeling them as random variables. The optimum service life of concrete structures can be defined as the time at which the probability of corrosion reaches an acceptable value for different types of concrete, reinforcing steel and concrete cover depths. The time-dependent probability of corrosion of reinforcing steel embedded in concrete structures is formulated as a nonlinear optimization problem that is solved by the projected Lagrangian algorithm. The example of a concrete bridge deck is used to illustrate that the timedependent probability of corrosion increases with temperature by 37% and 77% for life cycle temperature rises of 3◦C and 6◦C, respectively compared to the reference case at 23◦C. To reduce this probability of corrosion, corrosion-resistant steel reinforcement, high performance or/and higher concrete cover depth can be used. For the case of bridge decks reinforced with corrosion-resistant steel, the corrosion probability is reduced by half after 40 years compared to that associated with black steel for the climate scenario with 6◦C temperature rise. The final selection of an optimal design should take into account the life cycle costs incurred during the service life or life cycle of concrete infrastructures.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".