Wood <sup>ST</sup> : A Temperature-Dependent Plastic-Damage Constitutive Model Used for Numerical Simulation of Wood-Based Materials and Connections
Bibliographic record
Abstract
The thermomechanical behavior of members and connections plays a crucial role in the fire safety of timber construction. In this study, a unique constitutive model, WoodST, combining a number of mechanics-based submodels was developed for numerical simulation of wood-based materials and connections under forces and fire. The extended Yamada–Sun strength criteria were utilized to judge the brittle failure or ductile yielding in different directions and stress conditions. A strain-based damage evolution was developed to describe the postpeak softening of brittle failure in tension or shear. A plastic flow and a hardening law were established based on the strength criteria to depict the plastic stress-strain relationship of ductile yielding in compression. A strain-based hardening evolution was developed to implement a second hardening (densification) under compression perpendicular to grain. A multilinear reduction model was adopted to represent the influence of fire on the mechanical properties of wood-based materials. The developed model was used to model the structural response of a laminated veneer lumber (LVL) beam and a glulam bolted connection under force and fire. It is demonstrated that the proposed constitutive model was capable of simulating the thermomechanical response of LVL beam and glulam connection under force and fire within 10% difference between modeling and testing results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".