A Fuzzy Framework for Contractor Selection on IPD Projects
Bibliographic record
Abstract
The construction industry is characterized by complexity, budget and schedule overruns, quality and safety problems, and increased claims and disputes.To successfully manage the inherent complexity of construction projects, optimal contractor selection is integral for project success.Choosing the best-fit contractor is especially important in Integrated Project Delivery (IPD), since this procurement route relies heavily on the efficient collaboration of project stakeholders and necessitates trust to guarantee successful outcomes.However, the numerous methods and tools for contractor selection in the literature target traditional delivery routes and are unsuitable for IPD, considering the latter's distinct features and stakeholder roles.As such, owners transitioning to IPD do not fully understand the requirements for optimal contractor selection, which jeopardizes the success of IPD projects.To address this need, this paper conducts a comprehensive literature review and investigates twelve unique IPD case studies to identify contractor selection criteria important to IPD.The paper presents a decision-making framework for contractor selection in IPD projects, using the Fuzzy Inference System (FIS), that provides an indication of the best-fit contractor for the IPD project.This research fills a significant gap in the literature by providing a tool to assist IPD practitioners to select the right contractor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".