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Record W6999686329

Development and assessment of non-destructive evaluation techniques for the measurment of stress and strain in biological materials

2007· dissertation· en· W6999686329 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2007
Typedissertation
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Field (mathematics)Noise (video)Matching (statistics)Point (geometry)
DOInot available

Abstract

fetched live from OpenAlex

The heterogeneous and anisotropic nature of wood material creates additional design challenges not present with the use of other structural materials such as steel and aluminum. The natural variation in the physical properties of wood members requires that the specified strengths and resistances used for design calculations be based on the quantities measured for the fifth percentile of all wood materials tested. The result is that design may be unnecessarily conservative and subsequently inefficient.\n\nThe same properties that cause uncertainty surrounding the physical properties of biological materials also create difficulty in applying non-destructive evaluation techniques. Strain measurement is one particular technique that is extremely valuable for materials of known and consistent stress-strain relationships, but whose usefulness is diminished when applied to biological materials.\n\nTo demonstrate the need for more accurate strain measurement in light-framed structures, the predictive calculations and structural modelling of a post-framed building was compared to its demonstrated performance. The analysis did not adequately reflect the actual performance of the building, and it was determined that additional monitoring of light-framed buildings through systems such as strain measurement was required to gain a better understanding of the performance characteristics in order to optimize evaluation techniques.\n\nThis project aimed to develop a system that accurately measures strain in dimensional lumber of different types, which in turn will enable researchers to enhance monitoring the performance of light-frame structures and optimize design analysis and structural modelling techniques. The development of a methodology that provides a practical means by which to perform in-situ testing of post-frame buildings and decreases the complexity of post-frame building monitoring will contribute to the advancement of design and analysis techniques.\n\nIn the calibration phase of the project, metal foil resistance strain gages were mounted onto wooden specimens with dimensions of 5 x 13 x 40 mm, 5 x 40 x 100 mm, and 2 x 20 x 50 mm, and acrylic specimens with dimensions of 3 x 25 x 75 mm. These specimens were then subjected to loading in an ATS universal testing machine in the Physical Properties Lab at the University of Manitoba. Stress-strain curves were developed based upon the observed stress and strain levels. These calibrated gages were then mounted on to a 38 x 89 mm specimen of S-P-F dimensional lumber which represented a typical light-framed building material. This assembly was then subjected to a similar loading procedure as the calibrated gage and stress-strain curves were generated once again.\n\nThe slopes of the stress-strain curves developed from the two phases of the project were compared to determine if a consistent correlation existed. The three sizes of wood specimens did not demonstrate a consistent correlation. However, the acrylic specimen demonstrated consistent correlation amongst two groups of three with correlation coefficients within a forty percent range in one group and within a nine percent range in the other group. This suggests that further experimental refinements could produce the desired results.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.270
Teacher spread0.229 · 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 designBench or experimental
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

Citations0
Published2007
Admission routes1
Has abstractyes

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