An evaluation of architects' readiness for conducting energy modelling using BIM tools to achieve high energy performance buildings in the UK and Canada
Bibliographic record
Abstract
Buildings, consume more than 30% of the world's energy and is the world's largest energy consuming sector, contributing nearly a quarter of the total global greenhouse gas emissions. Global warming is the result of emission of greenhouse gases, and this represents a significant existential crisis. The effective design of buildings is one way to mitigate this issue and this starts with the design of the building. One of the architect's main responsibilities is the building’s geometric design, which has a considerable impact on energy consumption. Building Performance Analysis (BPA) is generally conducted during the later design stages often in support of the mechanical and electrical design, such as heating and cooling systems. To achieve a High Energy Performance Building (HEPB), this research considers the potential impact and implementation of a process which might bring the geometric design stage and energy analysis stages closer to each other. While architects usually deal with geometrical design, much of energy performance analysis work is carried out by consultant energy specialists. However, new BIM tools have the potential to make this stage of analysis more accessible to architects, who may not have specific building physics knowledge. The purpose of this study is to assess the acceptability of BIM based energy analysis tools to architects and assess their potential use in early stage energy analysis undertaken by nonspecialist architects. The aim of this research is to evaluate the conditions of the design process for HEPB in the UK and Canada and develop a series of recommendations to better enable architects to address energy efficiency in the early stages of the design process by using BIM tools. An abductive research approach is used to test existing theories regarding the ability of BIM to design and analyse green buildings. The survey of UK and Canadian architects identifies issues such as; standards, underlying knowledge, client demand and the use of BIM tools to identify applicability of the approach. The results from the study are used to understand the processes of HEPBs architectural design, including the sources and tools which are used. The respondents’ familiarity with BIM, its tools and ability for doing tasks in the design and construction industry, specifically regarding HEPBs design and the potential barriers for employing BIM are also considered. The recognised gap in the knowledge is to develop a better understanding of the issues of the detachment of architects as first designers of buildings involved in geometrical design from the later stages (Building Performance Analysis) and the possible solutions that might be provided by BIM tools. The contribution to knowledge of the research focuses around a better understanding of the specific barriers for the implementation and use of BIM energy analysis tools by architectural practices which will be achieved through finding weaknesses in the current process of design process and discovering potential solutions.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".