On the Quantification of Robustness of Structures
Bibliographic record
Abstract
The paper first reviews different interpretations of robustness. On this basis objectives facilitating the quantification of robustness of engineering systems are formulated. Thereafter a generic framework for risk assessments of engineering systems is presented in which robustness is related to the ability of the system to sustain damages. This framework is then applied to quantify robustness of structural systems and to develop a robustness index facilitating a consistent ranking of structures according to their robustness. The proposed approach to the assessment of robustness principally takes into account the effect of redundancy, ductility, damage and failure consequences as well as strategies for condition control and intervention during the service life of structures. Finally, a simple example illustrates the use of the framework for the assessment of the robustness of a jacket steel structure subject to fatigue damage. The example shows that presently used indicators for the robustness of jacket type steel structures such as the RIF only capture part of the picture and illustrates the merits of a risk based framework for robustness assessments.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".