Intelligent Estimation of Construction Project Costs Based on Subtractive Clustering-based Self-Learning Convolutional Neural Network
Bibliographic record
Abstract
Traditional construction project cost estimation methods rely on expert experience and statistical models, which are difficult to handle complex data and multimodal features effectively and have low prediction precision.This paper constructs an intelligent building engineering cost estimation model that combines subtractive clustering, a self-learning mechanism, and convolutional neural networks (CNN) to address this problem.In the data preprocessing stage, subtractive clustering is applied to optimize multimodal data, screen key features, and eliminate redundant information.Subsequently, the model parameters are dynamically adjusted according to the error feedback through a self-learning mechanism to improve its adaptability to diverse construction projects.In the feature extraction and estimation stage, the CNN module is combined to extract deep features from images, texts, and numerical data to achieve high-precision estimation.The experimental results show that the model in this paper outperforms traditional methods in terms of MSE (mean-square error), MAE (mean absolute error), R (coefficient of determination), MAPE (mean absolute percentage error), with the mean values being 73.18, 8.33, 0.9477, and 5.33%, respectively.In summary, the model in this paper demonstrates superior precision, adaptability, and robustness in construction project cost estimation.
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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 | 0.001 | 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 | 0.001 |
| 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".