An Effective Optimization of Time and Cost Estimation for Prefabrication Construction Management Using Artificial Neural Networks
Bibliographic record
Abstract
The success of every construction business relies on the projects performed in a given period and at the negotiated rate. The construction business includes prefabrication firms, logistics companies, design industries on site and so on. The manufacturing method includes the assembly of structural parts at a development plant and the transport of them onto the building site as finished or semi-assembled components. For optimization, artificial neural networks (ANNs) are used because of their capacity to overcome qualitative and quantitative difficulties in the building industry. An ANN is used to execute the input, hidden, and output layers depending on the weight of the hidden layer. Different modeling strategies maximize the layers. ANN covers a wide variety of issues in construction management, for instance cost analysis, decision making, prediction of the mark-up percentage and the production rate in the construction industry. The main advantage of prefabricated methodology is that the procedure is easily done. The other real benefit of the prefabrication process is its integrated versatility. The present study underlines that the total project period and cost are the key considerations in the current job procurement phase in constructing prefabrication. The results of the proposed model are based on ANN algorithms which mainly achieves the perfect weight values in time and cost estimates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".