System of Multiple ANNs for Online Planning of Numerous Building Improvements
Bibliographic record
Abstract
The aging infrastructure in North America and worldwide mandates large investments in repair and improvement (R&I) activities. For organizations that own many assets, managing a large number of R&I activities is not a simple task and requires accurate estimating and scheduling so that proper budgeting and resource allocation decisions can be made. To support these decisions, this paper introduces a Web-based system that estimates the cost and duration of a user-requested R&I activity and provides alternative schedules based on resource availability. For estimating, the Web-based system hosts 32 artificial neural networks (ANNs), trained on actual historical data, for 32 common R&I activities in building projects. Each ANN incorporates a sensitivity analysis to consider the uncertainty in the input parameters on the estimate, and is linked to a central scheduling algorithm for resource allocation based on a first-come first-serve basis. The automated system helps practitioners in planning numerous R&I requests with least time, cost, and paper work. Details on system development are provided in this paper along with perceived benefits and the opinion of users on its performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".