Risk-Based Comparison of Collaborative Delivery Methods in Canadian Construction: Progressive Design Build, Integrated Project Delivery and Project Alliancing
Bibliographic record
Abstract
In response to an aging infrastructure network, population growth, and the rising complexity of project delivery, Canada's infrastructure procurement landscape has undergone significant transformation in recent years.In light of this, collaborative project delivery methods, including Progressive Design-Build (PDB), Integrated Project Delivery (IPD) and Project Alliancing (PA) have recently gained traction in Canada as alternatives to traditional models by fostering teamwork, aligning commercial outcomes, and promoting collective accountability among stakeholders.This paper provides the results of a targeted review of existing literature on risk management characteristics of PDB, IPD, and PA across four dimensions: risk allocation and mitigation strategies, risk sharing and incentives and flexibility/adaptability in managing risk.The analysis emphasizes how these models address inefficiencies in traditional delivery systems by promoting collaboration and aligning risks and rewards equitably.To analyze the extent of adoption of the aforementioned delivery methods in Canada, this study surveys Canadian projects that have implemented PDB, IPD, or PA.The compilation of these projects forms a foundational database that supports future research on the influence of risk management practices on collaborative project delivery adoption and implementation in the Canadian context.This study identifies distinct risk management approaches across PDB, IPD, and PA, shaped by their underlying contractual frameworks.Preliminary findings from Canadian projects suggest that delivery method selection is influenced by sector-specific risk profiles and the level of risk integration each delivery method supports. of Metropolitan Montreal, 2023).These delivery methods emphasize early stakeholder involvement, shared risk/reward mechanisms, and a commitment to collaboration-elements that are critical in addressing Canada's infrastructure challenges.On the same hand, the Canadian Council for Public-Private Partnerships (CCPPP) has highlighted the growing relevance of these collaborative models as alternatives to traditional procurement methods, particularly in municipal contexts (The Canadian Council for Public-Private Partnerships, 2024).The selection of these three methods as the focus of this research is grounded in their position between traditional and fully privatized procurement methods, indicating their ability to optimize collaboration between public and private sector partners while maintaining sufficient owner control over project outcomes, as depicted in the CCPPP's latest guide for municipalities (The Canadian Council for Public-Private Partnerships, 2024).While most existing research focuses on collaborative project delivery methods in the U.S (Alleman & Tran, 2020; D. D. Gransberg, 2023;Ma et al., 2022;Rashed & Mutis, 2023) and other international contexts (Australian Government & Department of Infrastructure and Regional Development, 2015; Department of Treasury and Finance, 2010), there is limited exploration of how these collaborative models are applied in Canada.Therefore, this research explores Canadian projects that have implemented these project delivery methods, laying the groundwork for a database of case studies, facilitating future research on risk management in collaborative delivery methods.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".