Mass Timber Productivity- the Significance of Reduction in Non-Value Add Activities during On-Site Installation Sequence.
Bibliographic record
Abstract
The recent uptake of Mass Timber (M.T.), a prefabricated timber panelised form of construction, provides a potential sustainable resource to facilitate improved productivity outcomes to the construction industry. However, in Australia as well as U.S. and Canada, M.T. is in its infancy and there is a lack of empirical information available to industry. This consequently has resulted in reluctance by contractors and professionals to uptake this new innovative system. The aim of this paper is to undertake a comprehensive review of the on-site productivity outcomes. A quantitative Case Study approach was implemented by way of time-lapse digital video recording of three M.T. multi-storey buildings located in NSW, Australia. Crane cycles of the installation of the M.T. prefabricated panels were found to be the most representative and repeatable process and as a consequence were used to measure the M.T. productivity. Discussion is provided on potential areas of process and consequential productivity improvement. The M.T. crane cycles productivity at an Activity Level compared to M.T. productivity at Project Level revealed large differential between the two levels. The quantum of Non-Value Add activities was found to be a significant factor in the overall Project Level productivity outcome. This review paper undertakes a review of the outcomes of the case studies on the M.T. installation on three multi-storey buildings, the factors found that affected the resultant on-site construction productivity and its resultant beneficial implications to the construction industry.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".