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Record W7133016535

Relaxation based analysis of structures containing frame elements and walls

2003· dissertation· W7133016535 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.

fundA Canadian funder is recorded on the work.
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

VenueTSpace · 2003
Typedissertation
Language
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsRelaxation (psychology)Shear (geology)Stiffness matrixStiffnessDirect stiffness methodFrame (networking)Finite element methodDisplacement (psychology)
DOInot available

Abstract

fetched live from OpenAlex

The core of this research is the RELAXATION program written in C++. This program performs linear analysis for structures subjected to static loads. These structures can be composed using frame elements and shear walls. The basic element for modelling shear walls is a triangular element. Linear analysis is performed by an incremental displacement method or joint relaxation technique. The explanations that show how to generate the stiffness matrix of a triangular element with six d.o.f. per node, are given. In addition, the validity of this triangular element is demonstrated. The use of an over-relaxation factor is shown to greatly improve performance. Using examples the following is explained: the impact of this over-relaxation factor on time execution; how to prepare the input file and how to read the output file. Analysis of complex structures using the RELAXATION program, show to be about twice as fast as traditional stiffness method calculations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.284
Teacher spread0.274 · 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