Reliability-based life cycle design of resilient highway bridge
Bibliographic record
Abstract
Highway bridges are critical links in Canada’s transportation network, which enable personal mobility and transport of goods that support trade and economic development of neighbouring communities. Highway bridges should be designed and maintained to last at least 75 years with minimum maintenance. The average service life of bridge structures vary from 30 years to 100 years, which are continually extended by using different maintenance and rehabilitation strategies. Different technologies are used for bridge life extensions, including different combinations of protective systems, repair, strengthening, rehabilitation, and replacement actions of decks, superstructures, substructures and entire bridges. The growing concerns with aging bridges, increased load and reduced strength, environmental protection and vulnerability to extreme events require the development of resilient transportation infrastructure that minimizes traffic disruption and ensures social, economic and environmental sustainability and resilience over the entire life cycle of the bridge. Given the considerable uncertainty that is associated with the key parameters and physical models that affect the life cycle performance of highway bridges, there is a need to develop robust mechanistic and stochastic models to predict the service life of bridges. This paper presents a practical reliability-based approach for the life cycle design of resilient concrete bridges that enables to achieve long life bridges with an acceptable probability of failure, which minimizes traffic disruption and reduces the life cycle costs to the bridge owners and users. An example illustrates the benefits of implementing a life cycle-based design approach through the construction of high performance concrete highway bridge structures that yield lower risk of failure when compared to conventional normal concrete construction, in terms of lower traffic disruption, life cycle costs to the bridge owners and users; lower CO2 emissions and volume of construction waste materials; and reduced accident costs.
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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 | 0.001 |
| 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 | 0.000 |
| 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".