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Record W2373506833

ULTIMATE BEARING CAPACITY ANALYSIS OF DERRICK STEEL STRUCTURES BASED ON PARTIAL MODEL UPDATING THEORY

2007· article· en· W2373506833 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsStructural engineeringBearing capacityNonlinear systemCorrectnessBucklingBearing (navigation)Grading (engineering)EngineeringComputer scienceAlgorithmArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

For the ultimate bearing capacity analysis of derrick steel structures, a novel method is proposed based on the partial model updating theory, in which, the test stresses of the main load-bearing member bars are taken as the key indicators and the relevant design parameters as the input updating objects. The dynamic grading load test was performed on the model of laboratory derrick steel structures; the experimental results are compared with the simulative values and an satisfactory agreement is found; the error is within 5%, which verifies the correctness and feasibility of the theory. In addition, the simulation model for some type of derrick steel structures in service is established in association with the grading load test data and the proposed theory; the analog and experimental results show clear evidence of the mechanical behavior being truly reflected. Then the ultimate bearing capacity, failure shape and dangerous position are obtained using the linear buckling, geometric nonlinear and double nonlinear methods.

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 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: none
Teacher disagreement score0.625
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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