Effect of critical parameters on structural performance of balloon-type self-centering mass timber wall system
Why this work is in the frame
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Bibliographic record
Abstract
This study analyzed the structural behaviour of post-tensioned unbonded cross-laminated timber (UPTC) shear walls, employing a synergistic methodology that integrates numerical simulation with advanced machine learning (ML) techniques to deliver predictive insights on stiffness of balloon-type mass timber wall systems. It focused on assessing the influence of key structural parameters, including wall thickness, aspect ratio, tendon diameter, and post-tension stress, on the initial stiffness of UPTC shear walls. The findings revealed that enhancing wall thickness substantially increases the wall resistance to lateral loads, while the aspect ratio was identified as a significant factor in structural rigidity. Using ML, SHAP analysis emphasized the importance of wall dimensions and tendon stress for stiffness predictions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it