Economic Model for Assessing the Return on Investments in Structural Health Monitoring Systems
Bibliographic record
Abstract
urpose: The purpose of this article is the description of the approach to the economic assessment of a highly-effective system for state monitoring of structures ensuring an increase in safety and economic efficiency for utilization of complex engineering structures and buildings considering all existing risks. Design/Methodology/Approach: The essence of the approach is in obtaining the state control data of these structures and buildings from sensors, which detect hidden damages and cracks, monitor consequences of shocks, corrosion, tension, and overheating. Findings: All the collected data make up the predictive analysis using artificial intelligence, which can and must analyze this data in real-time mode. Practical Implications: Such a way for monitoring allows for assessing the state of the structures and repairing or replacing them before the critical moments occur, thus significantly reducing the cost of servicing data from complex engineering objects, as well as it ensures their reliability and safety. Digitalization should be introduced in all of the industrial sectors, including aviation, where effectiveness, reliability, and safety are closely interconnected. Originality/Value: Thanks to the development of the state monitoring systems and the economic efficiency of their use in critical structures, the possibility, and intensiveness of their improvement are growing. This has great value and pushes modern productions forward.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".