Integrating Decision Support System (DSS) and Building Information Modeling (BIM) to Optimize the Selection of Sustainable Building Components
Bibliographic record
Abstract
One of the challenges in sustainability analysis and its development is the optimum selection of sustainable materials to meet the project’s requirements while doing sustainable design. This can only be achieved when project team adopt the use of a strategic approach while selecting the materials, although this could be a complex task for decision makers. Building Information Modeling (BIM) offers designers the ability to assess different design alternatives at the conceptual stage of a project. As a method of integration and through its modeling techniques, BIM can be used to assess the impacts of design alternatives on the energy saving of buildings all over their life. Furthermore, BIM has the potential to help designers select the right type of materials during the early design stage, and make vital decisions when selecting the materials that have sustainable impact on the building’s life cycle. The main purpose of this study is to propose a methodology that integrates BIM with decision-making problem-solving approaches (i.e. Entropy-TOPSIS) in order to efficiently optimize the selection of sustainable building components at the conceptual design stage of building projects. Therefore, a Decision Support System (DSS) is developed by using Multiple Criteria Decision Making (MCDM) techniques to aid the design team decide on and select the optimum type of sustainable building components and design families while doing conceptual design of proposed projects, based on three main criteria (i.e. environmental factors, economic factors—“cost efficiency,” and social well-being) in an attempt to identify the influence of design variations on the whole building’s sustainable performance. The multi-criteria procedure embedded in the DSS relies on numerical models to simulate alternative situations, as well as ranking the alternatives and select the best ones based on both the owners’ strategic preferences and the availability of sustainable materials in the market. The set of models included in the DSS describes the relationship between sustainability criteria, manufacturers’ sustainable materials and the interactions between project team that take place during the design of sustainable building projects. This paper aims at exposing the feasibility of using BIM for analysing the life cycle costs of sustainable buildings at the conceptual stage. The design alternatives suggested by the DSS are evaluated in an integrated environment that joins BIM concept and Life Cycle Cost (LCC) method to analyze the operational cost of the whole building. An actual building project is used to validate the workability and capability of the proposed methodology.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".