Building Information Modeling for Environmental Impact Assessment in Early Design Phases: A Literature Review
Bibliographic record
Abstract
The building sector is the largest consumer of energy in industrial countries. Saving energy in new buildings or building renovations can thus lead to significant global environmental impacts. In this endeavor, building information modeling (BIM) and building energy modeling (BEM) are two important tools to make the transition to net-zero energy buildings (NZEB). So far, little attention has been devoted, in the literature, to discuss the connection between BIM, BEM, and Life-cycle assessment (LCA), which is the main topic of this article. A literature review of 157 journal articles and conference proceedings published between 1990 and 2020 is presented. This review outlines knowledge gaps concerning BIM, BEM, and environmental impact assessment. It suggests that defining the process with the right technology (at the right time) would result in a more integrated design process (IDP) and bridge current gaps. The most efficient way to improve process and technology is related to the competences of the architects, engineers and constructors (AEC). The review also indicates that the IDP in the early design phases (EDP) is in need of improvement for architects and engineers, where a better connection between design phases, specific levels of development (LOD) and BIM tools is needed. Competences, process and technology are the three main themes addressed in the review. Their relation to design phases and LOD is discussed. The aim is to propose possible solutions to the current hinders in BIM-to-BEM (BIM2BEM) and BIM-for-LCA (BIM4LCA) integration.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".