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Record W2783148942 · doi:10.14359/51714475

Partial Material Strength Reduction Factors: for ACI 318?

2019· article· en· W2783148942 on OpenAlexaff
Tong Zhang, F. Michael Bartlett

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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