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Potential Loss in Prestressing Tendon Forces under Long-Term Service Conditions: Cross-Laminated Timber Shear Wall Applications

2021· article· en· W4200546282 on OpenAlexaff
Minjuan He, Xiuzhi Zheng, Frank Lam, Zheng Li

Bibliographic record

VenueJournal of Structural Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCreepStructural engineeringShear (geology)Service lifeMaterials scienceShear forceGeotechnical engineeringDeformation (meteorology)Composite materialEngineering

Abstract

fetched live from OpenAlex

The seismic performance of post-tensioned cross-laminated timber (CLT) shear walls depends on the existing prestressing tendon force. The prestressing force will change over time because of the time-dependent elastic, creep, and environmental deformation of timber. In this study, post-tensioned CLT shear walls under three different prestressing force levels were monitored over 540 days to study the prestressing force loss under varied environments. The long-term experimental results of temperature and relative humidity, moisture content, prestressing force, and timber strain were carefully recorded and analyzed. Subsequently, a comprehensive numerical model was established which includes four modules: moisture diffusion analysis, time-dependent elastic deformation analysis, time-dependent creep and environmental deformation analysis, and prestressing force updating. The long-term experimental results were used to validate the established model. Good agreement between the simulated and the experimental results was achieved. Lastly, the validated numerical model was implemented to predict the potential loss of prestressing force in post-tensioned CLT shear walls during a service life of 50 years. The loss percentage under three different stress levels in uncontrolled environment scenario were 26.8%, 25.2%, and 21.1%. This information can be used to support the life cycle design of post-tensioned CLT structures ensuring the long-term structural safety.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of Structural EngineeringSame topicWood Treatment and PropertiesFrench-language works237,207