Mass Timber Projects in North America: An Exploratory Analysis
Bibliographic record
Abstract
Mass timber construction has gained significant traction in recent years, yet a comprehensive data-driven assessment of its design trends remains limited.This study analyzes 679 mass timber projects from the Woodworks Innovation Network (WIN) to examine the relationship between material selection, building use, and geospatial attributes.Through web scraping, key project characteristics-including location, building type, construction year, and material usage-were extracted, cleaned, and geocoded.Geographic data were integrated with FEMA's National Risk Index (NRI) to assess natural hazard exposure, though Canadian projects were excluded from risk analysis.Three analytical methods were employed: descriptive statistical analysis quantified material adoption trends, geospatial analysis mapped project distribution across hazard zones, and clustering analysis (using the K-modes algorithm) identified distinct material usage patterns.Results indicate that mass timber adoption has surged since 2010, with educational, office, and recreational buildings leading the trend.Type V and Type III construction dominated the permitting of the analyzed projects due to the ability to maximize the return and reduce the complexity of mass timber utilization.Geospatial analysis reveals regional variations in material preferences, influenced by hazard risks and policy factors.Clustering analysis identified four project groups with distinct material combinations and building characteristics, highlighting evolving design strategies.These findings provide a foundation for future research on mass timber's long-term performance, environmental impact, economic feasibility, and stakeholder networks.By leveraging this dataset, researchers can further investigate the role of policy, resilience, and innovation in advancing mass timber construction across North America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| 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) | 0.002 | 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".