Decision-Support Tool for Selecting the Best Delivery Method in Sustainable Construction Projects
Bibliographic record
Abstract
The construction industry's high resource consumption and greenhouse gas emissions emphasize the need for sustainable approaches.Sustainable construction minimizes environmental impact while balancing environmental, social, and economic priorities to promote long-term sustainability.A critical factor in achieving sustainable construction is the selection of an appropriate project delivery method (PDM).This study introduces a decision-support system (DSS) to guide the selection of the most suitable PDM for sustainable construction projects.Initially, a set of twelve key criteria for sustainable PDM selection is identified through an extensive literature review and expert consultations.These criteria are then weighted by importance based on survey responses from industry professionals specializing in sustainable construction.To evaluate the performance of four common PDMs (i.e., design-bid-build (DBB), designbuild (DB), construction management at risk (CMR), and integrated project delivery (IPD)), the experts were asked to assess each method's effectiveness against the selected criteria.Using the VIKOR method, a DSS application is developed to rank these PDM options according to the collected data.The developed DSS incorporates user preferences to generate tailored recommendations aligned with specific project needs and sustainability objectives.The proposed DSS is validated through application to two real-project case studies, wherein stakeholders provide input on criterion weighting relevant to their respective projects.Findings indicate that the application offers valuable insights for PDM selection in sustainable construction, enhancing decision-making and aligning project delivery with sustainability goals.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".