Economic Model for Assessing the Return on Investments in Structural Health Monitoring Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it