Particle Swarm Optimization and Tree Models (M5P) as Cost Estimation Tool for Construction Project
Bibliographic record
Abstract
A useful tool for non-linear multivariable modelling is the Artificial Neural Network (ANN).Cost-effectiveness has been demonstrated for the use of ANN.Typically, the neural network is trained using the back propagation (BP) algorithm.Even while this technique is particularly successful at training a wide variety of networks, it suffers from slow convergence and easy trapping in local minima.Regression tree models (M5P) were used to suggest the adjustment of neural networks utilizing particle swarm optimization (PSO) as a technique for predicting building costs due to the potential of multiscale modelling and prediction using the incorporated relevant parameters affecting the building cost.The three models were assessed using a variety of metrics, including Mean Square Error (MSE), absolute error, and Pearson's correlation as an accuracy criterion.The MSE was also employed as a parameter to determine the ideal number of neurons for the hidden layer.The results indicate that the PSO model outperforms the other two techniques.The study also concluded that the PSO model can accurately predict the costs of construction projects when considering specified project activities and price indices.A combination of project features and an automated modelling mechanism has the potential to provide a reliable prediction result.
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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 | 0.000 | 0.000 |
| 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".