Sustainable construction modelling: a systems engineering approach
Bibliographic record
Abstract
Modelling in the architecture, engineering, and construction (AEC) industry is a fundamental step for product and project delivery, and serves multiple purposes from visualization, communication, simulation, analysis, decision making, to performance assessment. For long, the AEC industry has used different types of modelling schemes. Typical examples include 2D and 3D models that capture geometric and spatial information, project schedules that capture sequence and durations, and process models that capture methodologies and work flows. With the advent of sustainable construction, it was realized that modelling in the AEC industry has to capture a new array of information and correlate such information to both the construction product and process. Examples of these information include embodied energy content, emission of pollutants, resource consumption, and noise and acoustics, just to name a few. Modelling for sustainable construction requires representing parameters pertaining to the environment and sustainability as well as different information related to the building geometry and its production method and execution plan. As part of a research undertaken by the authors, this paper proposes a systems-based model where: (1) the environmental system, (2) the building system (the product), and (3) the construction system (the production/management system) are represented as three interacting systems of systems that fundamentally exchange (1) energy, (2) matter, and in some cases (3) information, according to systems theory. While other modelling approaches are based on proceeding individually and sequentially in the process of evaluating environmental impacts caused by different multidisciplinary practices in the AEC industry, this systems-based model shall facilitate simultaneous evaluation of sustainability by the system as a whole. The focus on system flows of energy and materials shall highlight focus areas for impact mitigation and performance optimization as outlined in the paper. The Systems Modelling Language (SysML) is utilized to build the model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".