Monitoring of thermal deformation of cylindrical storage facilities with DInSAR
Bibliographic record
Abstract
Cylindrical storage infrastructure like silos, essential for agricultural commodities, can suffer significant structural failures due to long-term exposure, temperature changes and moisture. While initial designs consider thermal deformation, post-construction health monitoring often requires costly specialized equipments. Drawing upon prior applications of using Differential Interferometric Synthetic Aperture Radar (DInSAR) assessing thermal deformation in transportation infrastructure such as railways and bridges, this investigation extends the methodology to the evaluation of cylindrical storage structures, including both silos and petroleum tankers at multiple locations. This research presents an efficient and cost-effective framework utilizing DInSAR technology for monitoring the structural conditions of large cylindrical storage facilities. The investigation successfully captured seasonal thermal deformations, observing displacement cycles with a range approximately 10–15 mm for targeted cylindrical structures. Significant correlations were identified between DInSAR-measured deformations and the corresponding temperature variations, with fixed roof structures displaying the strongest correlation approximately 0.8. Operational structures, such as tankers and silos undergoing loading and unloading cycles, exhibited moderate correlation coefficients (within the range of 0.46 to 0.67). These preliminary results strongly support the potential of DInSAR for low-cost structural health monitoring of large cylindrical infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".