Green Building Performance Prediction and Evaluation System under BIM – based Digital Construction Technology
Bibliographic record
Abstract
Traditional green building performance evaluation methods usually rely on static design data and a single evaluation indicator, and lack dynamic monitoring and multi-dimensional data integration, which makes it dif icult to connect data at different stages, resulting in a large gap between prediction and actual performance.This paper applies digital construction technology to construct a green building performance prediction and evaluation system that integrates data across stages and dynamically, thereby improving the accuracy and reliability of the evaluation.First, according to Building Information Modeling (BIM) technology, a digital building model containing data such as energy ef iciency, environmental impact, and resource utilization is constructed.By deploying Internet of Things (IoT) sensors, energy consumption, temperature, humidity, and air quality in the building are monitored in real-time.The data is transmitted to the cloud platform for centralized processing and visualization, and compared with the design data in the BIM model to provide timely feedback on performance differences.Using big data analysis, support vector machine (SVM), and particle swarm optimization (PSO), data from the full life cycle are analyzed to predict building performance and optimize it.Finally, the LCA (Life Cycle Assessment) method is utilized to comprehensively consider the environmental impacts of buildings such as carbon footprint and resource consumption, and combined with multi-objective decision analysis tools to optimize the green building design and operation plan.The experiment shows that the energy ef iciency comparison difference of green buildings is within 10kWh/m², and all indicators are accurately predicted, providing a scienti ic basis for the design and operation of green buildings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".