Awareness, Perceptions and Willingness to Adopt CLT by U.S. Engineering Firms
Bibliographic record
Abstract
Cross-Laminated Timber (CLT) is an engineered wood-based product, developed in Europe in the early 1990s. CLT panels are made of multiple layers of wood boards oriented perpendicular to the adjacent layers. While CLT has been successful in Europe and is making its way into the Canadian, Australian, and other markets, it is in the early stages of adoption in the United States. This manuscript presents the results from research conducted to assess the market potential and barriers to the adoption of Cross-Laminated Timber in the United States, through the analysis of awareness, perceptions, and willingness to adopt Cross-Laminated Timber by the engineering community. Results from a survey of U.S. structural engineering firms shows that the level of awareness about Cross-Laminated Timber in the United States is low to intermediate. The perceived benefits of CLT are a favorable environmental and structural performance, and outstanding aesthetic properties. The perceived disadvantages are a lack of wide availability of CLT in the market and poor vibration and acoustic performance. Important barriers to the successful adoption of CLT, according to survey participants, are building code compatibility issues, initial cost, and its lack of availability in the United States market. Most respondents had a favorable response when asked about their willingness to adopt Cross-Laminated Timber in the near future, with more than half participants indicating that they would “very likely” or “likely” adopt the product. From these results, we conclude that the success of Cross-Laminated Timber construction in the United States will depend, in great part, on the information about Cross-Laminated Timber’s benefits reaching the target audience through promotional and educational initiatives and successful and prominent demonstration projects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".