Integrating Building Information Modeling and Life Cycle Assessment to Enhance the Decisions Related to Selecting Construction Methods at the Conceptual Design Stage of Buildings
Bibliographic record
Abstract
The construction industry, which is responsible for nearly 40% of global carbon emissions, is facing increasing pressure to adopt sustainable practices. Traditional construction methods often escalate resource depletion and waste generation, highlighting the need to prioritize sustainability. Life cycle assessment (LCA) is a significant tool for evaluating the environmental impacts of materials across different life cycle stages, yet its application is hindered by data complexities and uncertainties, particularly during the early design phases. Building Information Modeling (BIM) offers a transformative solution by centralizing and automating multidisciplinary data, thus streamlining LCA processes. This study addresses those existing gaps by proposing a structured methodology that integrates BIM with LCA to enhance their applicability during early design. The model leverages BIM’s capabilities to automate data extraction and enable real-time impact assessments by providing precise environmental evaluations of different construction methods. Focusing on modular prefabrication, 3D concrete printing, and conventional construction, this model comparatively evaluates environmental performance across different life cycle phases, highlighting distinct strengths and improvement areas. The Whole Building LCA reveals clear environmental differences, emphasizing modular construction’s substantial opportunities for enhancement to reduce critical impacts such as climate change and fossil depletion. This model supports decision-making, promotes circular economy principles, and aids the construction industry’s transition toward more sustainable practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".