Data Modeling Techniques for Pipeline Integrity Assessment: A State-of-the-Art Survey
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
Pipelines are economical and efficient modes of transporting oil and gas. Pipelines will inevitably confront various risk factors through their lifespan, which could lead to defects. Defects in pipelines can compromise the integrity of the pipeline systems and may result in catastrophic accidents. Thus, it is vital to conduct the integrity assessment of pipelines so that the safe operation of the pipelines can be ensured. Up to the present, widely used approaches for pipeline integrity assessment include defect characterization, growth rate prediction, and failure pressure evaluation. Although the theoretical developments of pipeline integrity assessment methods have yielded fruitful achievements and significantly benefit the industry practices, there is still a lack of a systematic review covering the whole process from data collection to model establishment of the pipeline integrity assessment. Therefore, a comprehensive review is conducted in this paper on the pipeline defect integrity assessment from the data modeling perspective. Firstly, the description of data required to construct pipeline defect integrity assessment models is presented, where the required data for modeling can be obtained from pipeline inspection measurements, monitoring sensors, testing experiments, etc. Then, different modeling techniques applied to pipeline integrity assessment are reviewed, which are classified into physics-based models, data-driven models, and multi-model fusion. Also, the advantages and limitations of these techniques are discussed. Finally, the possibility of applying the existing models to a digital twin of pipeline defect is explored. This paper aims to provide a guideline for researchers to select optimal models according to data availability and research requirements, which can benefit the research community, as well as, the industry.
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 | 0.001 | 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