Effect of Bearing Direction and Mounting Techniques on Cross-Laminated Timber Elements in the Field
Bibliographic record
Abstract
Vibration reduction index (Kij) measurements in the field have some challenges compared to laboratory measurements. Firstly, the measurement requires access to a construction site during the short time span when the cross-laminated timber (CLT) elements are apparent. Secondly, building contractors are often on a tight time schedule. Therefore, it is important to find a solution that minimizes the measurement time on site. Moreover, Kij measurements in the field include several types of junctions with different bearing directions which may be of importance. This paper aims to evaluate two different mounting techniques with accelerometers on CLT elements and to discuss how the bearing direction could affect the vibration level difference of junctions. Measurement data indicates few deviations between mounting techniques with bee wax or double-sided adhesive tape when accelerometers are attached on CLT elements. Furthermore, field measurements indicate that the vibration level will decrease with an increased number of lamellas over the same CLT element. Double-sided adhesive tape is an adequate substitute for bee wax in the field for mounting accelerometers on CLT elements, with some limitations for high frequencies. Measurement data concludes that the bearing direction of CLT elements can influence the vibration reduction index of a junction.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".