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Record W2783148942 · doi:10.14359/51714475

Partial Material Strength Reduction Factors: for ACI 318?

2019· article· en· W2783148942 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

VenueACI Structural Journal · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceReduction (mathematics)Composite materialStructural engineeringStrength reductionEngineeringMathematicsFinite element methodGeometry

Abstract

fetched live from OpenAlex

The strength reduction factors, phi, defined in ACI 318-14 for different structural actions and elements lead to inconsistent results. This study proposes partial material strength reduction factors for concrete, phic, and reinforcing steel, phis, that yield similar design strengths and more consistent reliability indices. Three structural actions are investigated: moment; shear; and, combined moment and axial force. The first-order, second-moment method is used to compute reliability indices for moment and shear, and Monte Carlo simulation is used for combined moment and axial force. The statistical parameters assumed for the professional factor for shear strength significantly impact the reliability indices. Although no single combination of phis and phic is the best for these three actions, the recommended partial material strength reduction factors are phis of 0.90 and phic of 0.60, or for spirally reinforced columns, 0.70. Alternatively, for shear, the combination with phis of 0.80 and phic of 0.65 is recommended.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.999

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.333
Teacher spread0.271 · 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