Multi-Criteria Decision Making to Improve Performance in Construction Projects with LEED Certification
Bibliographic record
Abstract
The Leadership in Energy and Environmental Design (LEED) Green Building Rating System, developed by the U.S. Green Building Council (USGBC) and later adopted by the Canada Green Building Council (CaGBC), has been widely accepted by public and private owners. Nevertheless it has been proven that adherence to LEED requirements has various effects on construction worker performance and productivity, construction cost and schedule and the environment. As a result, this limits the extent to which industry professionals apply the LEED principles, and are faced with difficulties in selecting the credits to be implemented in LEED certified projects. Therefore, there is a growing need to improve the sustainable goals by optimizing the LEED credit selection process to gain higher efficiency and productivity, which would result in increased popularity among contractors and design consultants. It has been identified that each LEED credit would have a different impact on cost, schedule, environment and the construction productivity. A considerable amount of literature has been published regarding these impact areas and it is clear that the impact is different from each credit and each project. However, very little research has been carried out that considers the combined effect of the identified factors. This paper describes the development of a multi-criteria prediction model that has the ability to model phenomena with significant uncertainty in inputs and multiple criteria such as project cost variation, the environmental impact, the impact on schedule and the impact on construction productivity. This simulation tool can be used by the design team at an early stage of the design process to optimise the benefits and minimise the negative impacts of LEED implementation in a new construction project.
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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".