Structural Design and Modelling Method for the Post-tensioned CLT Shear Wall Structures
Bibliographic record
Abstract
This paper presents the direct displacement-based design (DDD) procedure, structural modelling method, and structural performance calibration for post-tensioned CLT shear wall structures (PT-CLTstrs). Numerical models of the post-tensioned (PT) CLT shear walls were developed and calibrated with the experimental results. Based on the developed shear wall models, parametric analysis were conducted to investigate the lateral performance influencing factors. Then, a DDD procedure was developed and demonstrated by the design examples of a set of 8-, 12-, and 16-storey PT-CLTStrs. The corresponding simplified structural models were developed, and then a series of pushover and time-history dynamic analysis were conducted to calibrate the calculated structural performance objectives with the design targets of the DDD procedure. Finally, the empirical cumulative distribution functions (CDFs) of the maximum inter-storey drift (MaxISDR) were constructed. It is found that when the width of the PT CLT shear walls increases from 1.8 m to 3.0 m, the base shear at the drift of 2.0 % increases by twice accordingly. When the diameter of the PT strand increases from 15.2 mm to 34.6 mm, the base shear at the drift of 2.0 % increases by up to five times. Additionally, the MaxISDR limitation of the PT-CLTStrs is recommended as 2.2 % under the collapse prevention (CP) hazard level. The study results can serve as guidelines for the development of engineering design methods for the PT-CLTStrs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".