An Integrated Building Information Modeling and Life-Cycle Assessment Approach to Facilitate Design Decisions on Sustainable Building Projects in Canada
Bibliographic record
Abstract
In the context of the digital and sustainable transformation of building projects, the integrated approach of Building Information Modeling (BIM) and life-cycle assessment (LCA) has been widely studied. Recent advancements in such integrated modeling processes and techniques have not yet provided reliable and robust decision-making capabilities for designers to intuitively choose between material alternatives. This study develops a new design framework that integrates BIM, LCA, and multi-criteria decision-making (MCDM) algorithms to facilitate sustainable design processes in building projects. A case study using a single-family housing project in the British Columbia province of Canada was implemented to test the designs to demonstrate the effectiveness of the proposed integrated framework, including a three-way comparison of design scenarios—conventional hot-roll steel, recycled steel, and timber. The results indicate a significant performance improvement with the adoption of recycled structural steel materials, surpassing conventional steel designs and demonstrating a similar performance to that of timber designs. The study underscores the importance of informed decision-making in material selection, driven by the quantitative analysis of digital designs and multi-criteria evaluation (e.g., social carbon cost). This integrated framework offers a valuable tool for designers, engineers, and builders to achieve sustainability when designing building projects through the systematic and rapid comparison of environmental performance.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".