LEED–PDRI Framework for Pre-project Planning of Sustainable Building Projects
Bibliographic record
Abstract
Abstract Green buildings help in sustainability, in terms of achieving energy efficiency and minimizing the utilization of natural resources. Additional benefits include long-term sustainable building management and maintenance. There is, therefore, compelling motivation for the building of sustainable projects. This inspiration has led to the development of the leadership in energy and environmental design (LEED) rating systems and projects by the United States Green Building Council and the Canadian Green Building Council. Proper building project management (BPM) of such projects is warranted. Pre-project planning is a crucial part of BPM that ensures delivery and performance of construction projects. Pre-project planning is defined as the process that encompasses all the tasks between project initiation and detailed design. There is a positive relationship between comprehensive pre-project planning and enhanced project performance. Given the motivation for environmentally sustainable projects and proper management of constructing such projects, diligent pre-project planning for such projects is required. This paper investigates the use of the LEED rating system in pre-project planning of sustainable construction projects by developing a matrix that combines the LEED and the Project Definition Rating Index (PDRI) developed by the Construction Industry Institute. The conceptual matrix and its application to a case study demonstrates that the value of linking pre-project planning with LEED to improve the decision making process during planning and designing of building projects to improve sustainability.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".