Off-Site Construction Resilience: A Strategic Response to Inflation Challenges in Construction in Postpandemic Canada
Bibliographic record
Abstract
The COVID-19 triggered a period of inflation in Canada, with the Consumer Price Index increasing by an average of 3.4% in 2021, followed by 6.8% in 2022, and 3.9% in 2023. This rising inflation presents significant challenges for the construction industry, which accounts for over 1.5 million jobs and more than 7% of the country's economic activity. Two years after the pandemic, the inflationary environment was characterized by high, persistent, and volatile rates and influenced by factors beyond the industry's control. One effective approach to address these issues is to identify high-risk inflationary materials and create a price index based on historical market prices. Such a strategy allows project teams to track fluctuations in material prices and adjust project cost estimate. Embracing innovative strategies such as digitalized off-site construction and industrialized building systems can enhance the construction industry's resilience in the face of persistent inflation. This paper examines the impact of inflation on construction project budgets and project costs. Correlation coefficient tests are used to analyze the relationship between inflation rates and building construction prices. The results indicate a 14% increase in the contract sum attributable to inflation, a factor that may result in cost overruns. The findings point to the benefits of adopting off-site construction techniques as a strategic solution aimed at specifically countering the detrimental effects of cost overruns resulting from material waste and project delays, factors that not only escalate project costs but also significantly amplify the adverse impact of inflation on the project budget.
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.004 |
| 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".