Interdependence between Factors Influencing the Selection of Project Delivery Systems and Modular Construction
Bibliographic record
Abstract
The construction sector is going through a period of learning caused partly by the resurgence of prefabricated construction, particularly modular construction. In addition, this is due to the emergence of collaborative project delivery systems, such as Progressive Design-Build (PDB) and Integrated Project Delivery (IPD). Numerous studies indicate that these innovations can play a key role in addressing the challenges of the sector and improving project performance. However, the lack of knowledge and skills required hinders their adoption in construction projects. As a result, public owners find themselves in a situation of ambiguity in choosing the construction methods and contractual modes appropriate for their projects. The objective of this research is to identify, verify and evaluate the decision-making factors for the joint choice of the construction method and the delivery mode appropriate for the context of the construction project and aligned with the expectations of public owners. We aim to examine the interdependence between factors influencing the selection of modular construction and those associated with collaborative contractual modes, highlighting common factors as well as criteria specific to each approach. To do this, a systematic literature review is conducted. A list of 28 factors is identified. These factors are divided into five categories: Project characteristics, Owner characteristics, Owner's requirements, Owner's preferences, and External factors. The use of selection factors has the potential to make decisions more objective and to support project owners in managing their uncertainties. However, it is crucial to prioritise these factors according to their importance and to their impact on project performance. This topic will be the subject of future research.
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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 | 0.032 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".