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Record W2638304165 · doi:10.1016/j.proeng.2017.05.247

Calibrated Partial Factors for Support of Wedges Exposed in Tunnels

2017· article· en· W2638304165 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

VenueProcedia Engineering · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLimit state designEurocodeWedge (geometry)Probabilistic logicContext (archaeology)EngineeringLimit (mathematics)Code (set theory)Reliability (semiconductor)Computer scienceStructural engineeringCivil engineeringGeologyMathematics

Abstract

fetched live from OpenAlex

Geotechnical design is evolving to adopt the limit state design (LSD) philosophy, also known as reliability-based design (RBD). This is evident by its inclusion in geotechnical design codes (e.g. Eurocode 7). Partial factors are often used in design codes to overcome the difficulty in performing probabilistic analysis suggested by the RBD. The increasing use of RBD suggests a need to investigate the applicability of design with partial factors for various rock engineering structures; this paper will investigate their application in the design of support for a rock wedge in an underground opening. The paper provides a critical overview of the design philosophy of RBD, the components necessary for its application, and the methods by which the probability of failure may be computed. In addition, it discusses how partial factors are calibrated from RBD and how code development can be subsequently performed. This is put into context with a design example for the support of a rock wedge.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.414
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
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 science0.0010.000
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 head0.152
GPT teacher head0.344
Teacher spread0.191 · 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