Computational Intelligence Techniques for Building Transparent Construction Performance Models
Bibliographic record
Abstract
Building transparent and highly interpretable models of the construction performance is generally of significant importance to construction managers. However, previous research focuses more on the approximation accuracy of construction performance models. Few studies have been done on the transparency of models, i.e., offering some understandable cause-effect relationships between the construction performance indicator and its influence factors. In this paper, a transparent construction performance model is proposed. First, a neural network, named General Regression Neural Network (GRNN) is selected as the basic modeling technique. Its new genetic algorithm based learning algorithm is introduced. The GRNN not only presents a high approximation rate, but also offers importance indices about the influence of inputs on the output. Secondly, a fuzzy clustering algorithm is introduced to granulate the inputs into their linguistic terms. The model built with the use of granulated data provides clearer influence factors and the indicator of resulting construction performance. The proposed method is tested on the data collected from construction sites. The results demonstrate the feasibility and efficiency of the proposed 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".