Construction productivity assessment on Brock Commons Tallwood House
Bibliographic record
Abstract
Purpose The purpose of this paper is to improve the understanding of the impact of mass timber construction methods on construction performance through the successful delivery of the first-of-a-kind tall wood building, Brock Commons Tallwood House (Tallwood House). This paper is one of a set of papers examining the project; companion papers describe innovations used during the mass timber design and construction processes. Design/methodology/approach A mixed-method, longitudinal case study approach was used in this research project to investigate and document the Tallwood House project. Quantitative data were collected to perform the following analysis: hook time, the variability of productivity and schedule reliability. Members of the research team observed construction progress, meetings and decision-making, conducted periodic interviews and reviewed project artifacts. Findings The research presented in this paper is the culmination of a longitudinal study aimed at studying the innovation process on a project where radical innovations of structural systems were developed. Prefabrication, combined with the use of a virtual design and construction (VDC) model for planning and fabrication and early collaboration with trades, construction managers and consultants, increased the labor productivity of the on-site erection of the mass timber structural components and envelope panels and expedited the construction schedule. Originality/value This paper details an in-depth investigation into the construction productivity for a unique building project and lessons learned. The case study chosen is the construction of Tallwood House at the University of British Columbia. Tallwood House was the tallest mass-timber hybrid building in the world at the time of its construction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".