Sources of Challenges for Sustainability in the Building Design—The Relationship between Designers and Clients
Bibliographic record
Abstract
Sustainability demands have changed the building design nature increasing the diversity of requirements, activities, agents, and tools. The aim of this paper is to investigate the sources of challenges in the relationship between architectural and engineering (AE) design firms and clients for promoting sustainability in the building design. Additionally, this study investigated the building information modeling (BIM) deployment by the firms that supports sustainability. The research method adopted is qualitative and participatory, based on focus groups. Two groups were interviewed, eight AE design firms and six developers and/or construction companies, gathering the points of view of service providers and their clients. The identified sources of challenges around sustainability include lack of communication and imprecision of definition, requirements, and scope. Additionally, management issues include performance evaluation, traditional work relationships, tools, and processes that do not support collaboration needs. In addition, AE design firms’ organization affects the client relationship and design quality, including the consideration of sustainability issues in the design solutions. The sources are found in the AE design firm’s processes of strategy planning, business and marketing, design, people, and knowledge management.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".