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Record W2178402216 · doi:10.1139/cjce-2013-0491

A comprehensive collapse fragility assessment of moment resisting steel frames considering various sources of uncertainties

2015· article· en· W2178402216 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsnot available
FundersUC Berkeley College of Chemistry
KeywordsFragilityMoment (physics)Standard deviationQuality (philosophy)Monte Carlo methodUncertainty analysisMathematicsStructural engineeringApplied mathematicsEngineeringStatisticsPhysics

Abstract

fetched live from OpenAlex

Different sources of uncertainties contribute to the collapse and safety assessment of structures. In this paper, impact of construction quality (CQ) is considered in developing analytical collapse fragility curves for moment resisting steel frames. Furthermore, the interaction of this source of uncertainty with epistemic uncertainty inherent in modeling parameters, due to lack of knowledge and inaccuracy of predictor equations, is investigated. Beam strength, column strength, beam ductility, and column ductility meta-variables are defined as modeling parameters which are being suffered by informal uncertainty. Quadratic equations for the mean and the standard deviation of collapse fragility curves are derived by utilizing response surfaces, which are interpolated to analytically-derived values considering realizations for modeling variables and for various levels of construction quality. To the best of the authors’ knowledge, interaction of modeling and CQ uncertainty in analytical collapse fragility curve has not been considered in previous investigations. A fuzzy rule-based method is applied to employ the effects of uncertainty due to CQ. Using Monte Carlo simulation for the modeling variables and the construction quality index, and subsequently computing response surface coefficients via a fuzzy inference system, and finally deriving collapse fragility curve parameters through response surfaces, result in collapse fragility curves of structures. In developing these curves, different sources of uncertainties are involved, ranging from lexical to informal and stochastic types. It is concluded that neglecting the effects of these sources leads to the underestimation of collapse fragility probability. This shows the importance of considering modeling and construction quality uncertainty effects on collapse fragility curves. It is shown that for a sample moment resisting steel frame collapse probability is increased 53% and 60% for 10% and 2% probability of exceedance in 50 years seismic hazard levels, respectively, while interaction of CQ and modeling uncertainties are considered in comparison with neglecting them. Otherwise, if only modeling uncertainty is involved, this increment is evaluated at 42% and 16%, respectively for the aforementioned probabilities of exceedance.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.300
Teacher spread0.219 · 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