Developing an openBIM Information Delivery Specifications Framework for Operational Carbon Impact Assessment of Building Projects
Bibliographic record
Abstract
BIM (building information modeling) is widely recognized for enhancing the efficiency and precision of building energy modeling (BEM), primarily by reducing model development time and improving model accuracy. This paper presents a novel framework leveraging “openBIM” to standardize and harmonize BIM-driven solutions for energy simulations, facilitating comprehensive operational carbon impact assessments. Unlike existing approaches, our framework uniquely integrates information delivery specifications (IDS) with openBIM standards to define the minimum data requirements within the IFC schema, tailored to various levels of development (LOD). This innovation ensures consistent data exchange and interoperability across diverse energy modeling and simulation tools, addressing common challenges of data fragmentation and inaccuracy in operational carbon assessments. By advancing the current state of the art, the proposed framework empowers energy modelers, LCA analysts, and asset managers to streamline IDS implementation, fostering more efficient and reliable construction industry practices. This research thus marks a significant step towards achieving more sustainable building projects through enhanced data-driven insights.
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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".