Life-cycle cost analysis of concrete structures reinforced with stainless steel reinforcing bars
Bibliographic record
Abstract
The durability of reinforced concrete highway bridges is significantly affected by heavy road salt applications that are prevalent in parts of Canada and regions of the world with cold winter climates. Over time, chlorides migrate through the concrete to the reinforcing steel (rebar), resulting in corrosion and eventual loss of structural performance due to concrete spalling and loss of bond between the rebar and concrete. This causes significant reductions the service life of the structure. To address this issue, recent efforts have been undertaken to evaluate the use of corrosion resistant alternatives to traditional reinforcing steel, including stainless steel rebar. Along with assessing the increased durability that can be achieved, cost comparisons have been performed to identify conditions under which the increased cost associated with stainless steel rebar is warranted. In this paper, the results of recent analytical studies performed on the benefits of stainless steel rebar use will be presented. A significant gap identified in the literature has been the limited extent to which the effect of cracks in the concrete has been considered in assessing the corrosion performance of the rebar. In the current study, a probabilistic model for predicting the service life of reinforced concrete elements exposed to chlorides by Hartt (2012) has been modified to consider the effect of cracks on the diffusion coefficient, using a simplified approach proposed by Lu et al. (2011). In this paper, the modified model is described and used to demonstrate the effects of surface chloride concentration and the presence of cracks on rebar performance. This performance is characterized using a “critical cost ratio” below which it is economically appropriate, from a life-cycle cost perspective, to use stainless steel rather than black steel rebar.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".