Automated Creation of the Pipeline Digital Twin During Construction: Improvement to Construction Quality and Pipeline Integrity
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
The concept of the digital twin dates all the way back to the 1950’s when NASA, GE and other industrial manufacturers started creating abstract digital models of equipment to model their performance in simulations and maintain a record of the asset throughout its life span [1]. Over the years more and more industries have adopted the digital twin paradigm to improve traceability, maintenance, and analytics allowing for improved sustainment of the asset or equipment while reducing various risks identified during life cycle management. It has been found that collectively, the digital twin concept improves the overall net present value of an asset. The oil and gas industry has slowly been adopting the digital twin paradigm of asset life cycle management over the past two decades with the focus on facilities. Recently, field trials were completed to test and evaluate workflows and sensor platforms for the creation of a digital twin for pipelines. The trials resulted in highly accurate pipeline centerlines, weld locations, Depth to Cover (DoC) and ditch geometry capture in digital formats. This paper describes the methodologies used, and the results of an actual construction field trial with a comparison to traditional data collection methods for these attributes. The value of creating a pipeline digital twin during pipeline construction in near-real-time is discussed with an emphasis on the potential benefits to life cycle management and pipeline integrity.
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