A Hybrid Multi-Criteria Decision Support System for Selecting the Most Sustainable Structural Material for a Multistory Building Construction
Bibliographic record
Abstract
In recent years, the performance of the construction industry has highlighted the increased need for better resource efficiency, improved productivity, less waste, and increased value through sustainable construction practices. The core concept of sustainable construction is to maximize value and minimize harm by achieving a balance between social, economic, technical, and environmental aspects, commonly known as the pillars of sustainability. The decision regarding which structural material to select for any construction project is traditionally made based on technical and economic considerations with little or no attention paid to social and environmental aspects. Furthermore, the majority of the available literature on the subject considered three sustainability pillars (i.e., environmental, social, and economic), ignoring the influence of technical aspects for overall sustainability assessment. Industry experts have also noted an unfulfilled need for a multi-criteria decision-making (MCDM) technique that can integrate all stakeholders’ (project owner, designer, and constructor) opinions into the selection process. Hence, this research developed a decision support system (DSS) involving MCDM techniques to aid in selecting the most sustainable structural material, considering the four pillars of sustainability in the integrated project delivery (IPD) framework. A hybrid MCDM method combining AHP, TOPSIS, and VIKOR in a fuzzy environment was used to develop the DSS. A hypothetical eight-story building was considered for a case study to validate the developed DSS. The result shows that user preferences highly govern the final ranking of the alternative options of structural materials. Timber was chosen as the most sustainable option once the stakeholders assigned balanced importance to all factors of sustainable construction practices. The developed DSS was designed to be generic, can be used by any group of industry practitioners, and is expected to enhance objectivity and consistency of the decision-making process as a step towards achieving sustainable construction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".