Design database development and comparative performance assessment of connections in cross-laminated timber construction
Bibliographic record
Abstract
The growing adoption of mass timber construction, along with the variety of building products, such as cross-laminated timber (CLT), calls for a comprehensive structural performance assessment for sustainability-based design purposes. On the other hand, the vast variety of mechanical joinery types necessitates a detailed and systematic understanding of connection mechanics. This investigation develops a comprehensive database of connections for mass CLT components, serving as a dynamic “dictionary” that could inform designers with optimal design scenarios. The database has collected, aggregated, and post-processed data related to mechanical performance from 522 sets of state-of-the-art connections used in mass timber construction practice and research worldwide, including 2048 associated test specimens. The database is utilized to systematically assess the performance of a credible range of commercially available timber joinery products, along with those developed through research and development in mass timber construction. The mechanical performance of the collected data is assessed through qualitative and quantitative metrics based on assembly, joinery, and fastener types under tensile and shear loading. The database is designed to be expandable and adaptable for a wide range of applications, enabling further exploration of the structural behavior of panelized mass timber systems. It also serves as a framework for developing and implementing performance-based, analytics-driven design methodologies. • Developed a comprehensive open-source database for CLT connection performance. • Analyzed 522 group sets and 2048 test specimens for structural performance insights. • Identified high-performing alternatives to conventional CLT shear wall connections. • Assessed qualitative and quantitative mechanical behavior of CLT joinery types. • Explored digital fabrication techniques to enhance CLT connection efficiency.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".