Bibliographic record
Abstract
Sustainable and durable infrastructure facilities, including bridges, require optimum use of all resources involving reduction of energy and water consumption during all project phases, including planning, design, construction, maintenance, operations, repair and rehabilitation, and finally decommissioning and disposal of the debris at the end of its service life. Design of a sustainable and durable bridge structure requires consideration of a few feasible alternatives to develop an optimum option to fulfill all of the relevant limit states, with the best life-cycle performance and with the lowest life-cycle costs. The current national standards do not account for the observed increases in operating loads and the increasing deterioration of bridge structures over their service life. While these standards emphasize quality control in choice of materials, design and construction, they do not provide guidance and scientific tools to design and maintain a bridge structure for durability over its service life, and include only prescriptive tools for preventing some deterioration modes. This research program integrates sustainability and durability in the design of a conventional bridge structure in a cold climate country, subjected to the various mechanical natural and man-made loads and an aggressive environment, and considers the performance of the various materials and structural components over the design service life. The latest available models of the relevant deterioration modes have been incorporated in the life-cycle performance and design considerations. The basic procedure adopts a multiple protection strategy for all deterioration modes, resulting from the relevant aggressive actions, and integrates durability considerations with structural calculations for the final design and defines maintenance strategies and any needed supplementary protection techniques. The design-for-durability procedure is illustrated in a worked out bridge design example.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".