MétaCan
Menu
Back to cohort
Record W1991159422 · doi:10.1115/omae2006-92095

On the Quantification of Robustness of Structures

2006· article· en· W1991159422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRobustness (evolution)Computer scienceReliability engineeringData miningEngineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.316
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it