Barriers to the Use of Cross-Laminated Timber for Mid-Rise Residential Buildings in the UAE
Bibliographic record
Abstract
Buildings account for approximately 40% of global energy consumption annually, with substantial energy use occurring during both the construction and operation phases. The energy required for the production of construction materials contributes significantly to the overall energy intensity of the building sector. This underscores the critical need for materials with low embodied energy to mitigate the environmental impact associated with building construction and operation. Cross-laminated timber, massive timber product with excellent load-bearing capabilities, is becoming popular in mid-rise buildings worldwide. CLT’s environmental, economic, and social benefits surpass traditional materials, and its use is widespread in Europe, America, Canada, and Australia. However, no mid-rise CLT buildings have been constructed in the UAE yet. This study aims to investigate and identify the barriers to adopting CLT as a building material and construction system for mid-rise buildings in the UAE. A qualitative approach is used to study stakeholders’ behavior towards CLT construction. A comprehensive questionnaire survey and conversational interviews are conducted, with the responses analyzed to identify patterns and themes. The results identify the existing barriers within the construction industry impeding the adoption of cross-laminated timber (CLT). Additionally, the study discusses strategies necessary to facilitate the widespread adoption of CLT. These findings will inform future research aimed at addressing the obstacles to constructing mid-rise buildings using CLT in the UAE.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".