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AN EXPERIMENTAL INVESTIGATION OF THE THERMOMECHANICAL PERFORMANCE OF WOOD STRUCTURES ASSEMBLED WITH DENSIFIED WOODEN DOWELS UNDER FIRE EXPOSURE

2025· article· ru· W7117566057 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

VenueInternational Journal for Computational Civil and Structural Engineering · 2025
Typearticle
Languageru
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCharringDowelFire performanceEngineered woodFire resistanceDeformation (meteorology)Thermal

Abstract

fetched live from OpenAlex

This study presents an experimental investigation into the thermomechanical behavior of wood structures assembled with densified wooden dowels under fire exposure. The research focuses on Adhesive-Free Engineered Wood Products (AFEWPs), particularly adhesive-free cross-laminated timber (AFCLT) panels, and timber connections incorporating either thermo-mechanically compressed wooden dowels or conventional steel dowels. A series of thermal and thermomechanical tests were conducted to evaluate internal temperature distribution, charring behavior, and structural displacement at elevated temperatures. The fire performance of dowel-type connections was assessed by comparing the thermal response and deformation of joints using wooden and steel dowels. The results indicate that timber connections incorporating densified wooden dowels exhibited better thermal insulation and lower charring rates compared to those with steel dowels, thereby improving the overall fire resistance of the jointed assemblies. This study highlights the potential of densified wood dowel as a sustainable and fire-resilient alternative to metallic fasteners in engineered wood structures.

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: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.011
GPT teacher head0.240
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