Case Study on the Failure of Polyurea Elastomer (PU) Coating in a Sea Water Storage Tank
Bibliographic record
Abstract
Abstract Coating system of a seawater storage tank failed during service at the humid south gulf coastline. The failure occurred during the summer of 2022 after only 14 months of service. The tank had an acknowledged life expectancy of 15 years. An investigation team was deployed to investigate the root causes of the coating failure. In this case study, a comprehensive investigation was conducted to discover the reasons behind the premature failure of the sprayed PU (polyurea) elastomer coating on a water storage tank during a turnkey project in the Arabian Gulf region. Visual inspections alongside longitudinal seams of shell courses revealed rust stains as well as blistering and delamination. DFT (Dry film thickness) checks revealed thickness variations beyond specified limits. Pull-off adhesion tests on the as-applied coating revealed inter-layer (i.e., cohesive) failure. In addition, the holiday checks revealed pin-holes near longitudinal weld seams. On the other hand, the FTIR (Fourier-transform infrared) spectrum of the applied coating was found in close resemblance with the polyurethane spectrum. Whereas, the water resistance test did not reveal significant deformation. The failure mechanism leading to the in-service failure of the applied coating is explained.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".