Modeling Hysteretic Behavior of Wood Shear Walls with a Protocol-Independent Nail Connection Algorithm
Bibliographic record
Abstract
This paper presents an extension to an algorithm called HYST to develop the hysteresis characteristics of a nail connection. The paper also discusses the implementation of the algorithm in a finite-element model of a wood shear wall, called WALL2D, to study the hysteretic wall response. The HYST algorithm is a protocol-independent and mechanics-based procedure that considers the nail shank as steel beam elements and the wood embedment medium as compression-only spring elements smeared along the nail shank. By accounting for the stiffness degradation of the wood embedment medium under cyclic loading, HYST can fully address strength/stiffness degradation and the pinching effect in the hysteresis of typical nail connections. HYST was verified by the load-slip hystereses from nail connections tested with two different loading protocols. The WALL2D application model consists of linear elastic beam elements for framing members, orthotropic plate elements for sheathing panels, linear springs for framing connections, and oriented nonlinear springs for panel-frame nail connections. The hysteretic behavior of the nonlinear springs is represented by the HYST algorithm. The wall model was verified by reversed cyclic test results of two types of shear walls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".