LIVE Digital Twin for Smart Maintenance in Structural Systems
Bibliographic record
Abstract
Instabilities and failure in many industrial structures can be too costly. That includes the pipeline structures for oil and gas industries or power generation plans and infrastructural transit systems. Prognostics and health management, along with Preventive, predictive, and prescriptive maintenance, are alternative options to avoid the failure in these systems by smart and on-time maintenance. However, although it is possible to collect data dynamically from these systems through their service periods, in many cases, a trustworthy and reliable knowledge base to allow making the right decisions is not always available. This paper presents the concept of LIVE Digital Twin that relies on four phases of Learn, Identify, Verify, Extend employing various Computer-Aided Engineering (CAE) simulation strategies during the life span of the structure parallel to its design, performance, inspection, and maintenance. The architecture of LIVE Digital Twin is presented, and the details are described along with some practical case studies in Light Rail Transit (LRT) and pipeline systems in oil and gas industries. The presented concept and architecture of LIVE Digital Twin can be employed and implemented for various other applications and non-structural systems.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".