Lifetime Serviceability and Safety of Highway Bridges Under Climate Change
Bibliographic record
Abstract
This dissertation aims to address the growing challenge of ensuring the serviceability and safety of concrete highway bridges amidst climate change, with a particular emphasis on the Canadian scenario where a significant backlog in maintenance exists. A particular focus is set on characterizing the accelerated deterioration of bridges due to climatic change and assessing its impact on bridge lifetime performance, from regular service to extreme collision scenarios. The research unfolds in three phases. First, it assesses the impact of climate change on chloride-induced damage, a significant deterioration factor for concrete bridges. This phase includes two parts: one is to establish a provincial database for Ontario that documents chloride exposure over time and different traffic and weather conditions. Additionally, the study utilizes machine learning to create Corrosion Hazard Maps, which reveal spatial variations in the risk of corrosion damage to bridges under different climate change scenarios. Building on the first phase which focuses on serviceability, the second phase evaluates how climate change exacerbates the safety risks associated with both service loads and extreme vehicle-bridge collisions. For service loads, reliability-based methodologies are developed to assess the time-varied safety of bridges, considering both traditional vehicular loads and emerging traffic patterns (i.e., automated truck platooning). In the case of vehicle-bridge collisions, the approach focuses on employing a fragility-based approach for evaluating the lifetime performance of concrete highway bridges that are exposed to both episodic (vehicle-bridge collision) and chronic (corrosion) hazards. The final phase expands the scope of the first and second phases from individual bridges to entire transportation networks. It introduces a multiscale, risk-based assessment framework that prioritizes bridge rehabilitation to accommodate truck platooning and mitigate collision risks. Overall, the dissertation promotes a comprehensive, resilient framework for bridge life-cycle management, integrating various stages from design to maintenance within the broader context of socio-economic factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".