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Record W7015469748

Standards on the assessment of existing timber structures - SIA 269

2010· article· en· W7015469748 on OpenAlexaboutno aff

Bibliographic record

VenueDORA Empa (Swiss Federal Laboratories for Materials Science and Technology (Empa)) · 2010
Typearticle
Languageen
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Process (computing)Risk assessmentQuality (philosophy)Impact assessment
DOInot available

Abstract

fetched live from OpenAlex

Guidelines for the use and the maintenance of existing structures exist in many countries. At least in the USA, Canada, Switzerland [SIA 462] and UK such guidelines have been prepared at a detailed level [Diamantidis]. At present however only a few countries (for example the Netherlands [NEN 8700] and Switzerland ([SIA 462], [SIA 469], [SIA 162/5], [SIA 2017], [SIA 2018], [SIA 269] [SIA 269/1]-[SIA 269/7] [SIA 469] [SIA 469] [SIA 469]) have or work out general applicable code-type documents for the assessment of existing structures. In 2001 the first edition of an ISO-standard [ISO 13822] on the assessment of existing structures has been approved.<br />The assessment process of existing structures is part of the life cycle of a structure. As can be seen from Figure 10, assessing existing structures clearly differs from designing new structures due to the amount and quality of available information. Figure 10 is taken from the Swiss Standard SIA 260 Basis of Structural Design [SIA 260] and represents the concept of the actual Swiss Codes for the design of new structures and the assessment of existing structures together with all relevant terms, situations, verifications and documents.<br />Codes for the assessment of existing structures should include [Diamantidis]:<br />• Area of application (incl. differentiation between assessment of existing parts of a structure and design of new parts or strengthening elements)<br />• General principles of assessment (incl. stepwise procedure)<br />• Methods for updating<br />• Methods and format for verification<br />• Risk acceptance criteria<br />• Guidelines for decisions and intervention planning.

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0100.006
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0040.004
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0270.051

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 head0.013
GPT teacher head0.305
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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