Bibliographic record
Abstract
A new type of mass timber structural system has been developed in New Zealand over the last decade. Timber members made of engineered wood products are used in combination with post-tensioning cables to produce highly efficient structural components suitable for multi-story moment resisting frames or shear wall-based lateral load resisting systems. Both systems are particularly useful in structures designed in high seismic regions. The post-tensioning also ensures self-centering of the components and the structural systems after a seismic event. In addition to the post-tensioning, the systems can use energy dissipating devices within the connections that further enhance the ductility of the systems and make them good candidates for low damage structural applications. Extensive experimental and numerical studies have been conducted to determine the performance of these systems and design procedures have been developed for practical applications. In an effort to bring this system closer to the North American designers, this paper contains a summary of the evolution of the concept and the most important research projects and findings to date. In addition, a number of applications within and outside New Zealand are reviewed to demonstrate the applicability of the concept. Finally, potential and recent initiatives for adoption of the technology in North America are discussed.
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.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".