Sustainability in Prefabricated Construction: Enhancing Multicriteria Analysis and Prediction Using Machine Learning
Bibliographic record
Abstract
Multicriteria analysis is widely used to prove the excellence of prefabricated construction compared with conventional construction. However, because previous studies have not presented the results of an integrated analysis, identifying the merits of prefabricated construction is challenging. Furthermore, clients experience difficulty when considering prefabricated construction owing to the complexity of simulations and the lack of data. Therefore, this study aimed to conduct a multicriteria analysis for prefabricated construction considering productivity, safety, environment, and economy, and develop a multi-prediction model. This study was conducted in five stages. Results revealed that prefabricated construction was superior to conventional construction for all variables, with the former scoring 0.0927 on average and the latter scoring 1.863. The multiprediction model utilizing a decision tree and Bayesian optimization has a high performance, achieving over 94%. Using study findings, decision makers can use the multiprediction model to assess the expected performance of prefabricated construction. This enables a comprehensive comparison of various conditions across different aspects through the multicriteria analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".