Quality control and quality assurance in hybrid mass timber high-rise construction : a case study of the Brock Commons
Bibliographic record
Abstract
Wood has seen a resurgence recently as a construction material driven by technological advances and a growing concern for the environment. Although an increasing amount of mass timber high-rises are being built all around the world, lack of information and outdated preconceptions are some of the obstacles that are keeping mass timber products from increasing their market share in high-rise construction. Academia and industry leaders must keep track of the progress that is being made and inform the general public as innovation and technological advances continue to take place. In this context, the University of British Columbia has recently completed the construction of the Brock Commons Tallwood House. This 18-story residence building employs two reinforced concrete cores and a mass timber structure composed of cross laminated timber panels, glued-laminated columns, and parallel strand lumber columns. With this, the building is currently the tallest wood building in the world and a testament to the suitability of engineered wood elements for high-rise construction. Aiming to address the lack of information surrounding mass timber high rise construction, this thesis documents the quality assurance (QA) and quality control (QC) practices that were put in place during the delivery of the building. The main objective of this research was to identify and present lessons learned from the application of these QA/QC practices. To do this, various QA/QC practices were identified and analyzed by reviewing the project specifications and other project documents, reviewing recognized industry standards, and interviewing various members of the project team. This study found a series of comprehensive and well-planned QA/QC practices that were put in place by the project team and that were appropriate to comply with the project requirements. This study concluded that most of these practices are replicable and advisable for future projects. The different QA/QC practices that were identified and the lessons learned from their application are presented in this thesis.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".