Bibliographic record
Abstract
Impacts of inevitable disasters and climate change have been major concerns for the safety and sustainability of communities in the recent past. In an effort to reduce these impacts, development of resilience in civil infrastructures is becoming crucial. Conceptually, resilience is the ability to absorb, recover from, and adapt to shocks or changing conditions. The current practice for infrastructure asset management needs to incorporate this concept of resilience in order to reduce or prevent the detrimental consequences not only to the physical infrastructure systems, but also to communities and other systems vital for fulfilling human needs. For example, consequences can include environmental impacts caused by an incident and rehabilitation construction activities, increased costs for the asset management, and degradation in the quality of life. Therefore, resilience thinking needs to be practiced for designing and managing civil infrastructure systems so that they are resilient to external stresses such as climate change and natural disasters. Despite the awareness that resilience can be a key to resolve the difficulties with extreme events and climate change and that geotechnical assets serve as crucial components in critical infrastructure systems, research in the resilience of geotechnical assets is lacking. To put resilience thinking into practical applications in geotechnical engineering, a quantitative-based framework suitable and applicable for geotechnical assets is necessary. \n \nA quantitative resilience assessment framework applicable for geotechnical assets is proposed in this thesis. Driver-Pressure-State-Impact-Response (DPSIR) framework is adopted in developing the framework. It quantifies the impacts of damaged geotechnical assets to the relevant civil infrastructure network subjected to hazard scenarios. It also evaluates which strategic planning for mitigation and rehabilitation against the hazards is the most effective way for improving the resilience of the geotechnical assets. Metrics which reflect robustness, rapidity, redundancy, and resourcefulness aspects of resilience are developed for the evaluation. Environmental, economic, and social impacts are also concurrently considered to understand the trade-offs between the response strategies and their implementation consequences. The proposed framework is demonstrated using a case study on road embankments in a transportation network connecting London and Toronto in the province of Ontario.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".