A critical and bibliometric review of life cycle cost analysis integration into decision support systems for pipeline asset integrity management
Bibliographic record
Abstract
• Maintaining pipeline integrity is essential for safety, environmental protection, and energy security. • Traditional pipeline management is reactive, leading to high costs, safety risks, and inefficiencies. • Life Cycle Cost Analysis-based Decision Support Systems (LCCA-DSS) improve pipeline management by optimizing costs and risks. • There is limited research on integrating LCCA and DSS for pipeline integrity, highlighting a major gap. • North America leads research in this field, while South America and Africa have minimal contributions. Pipelines play an important role in the worldwide oil and gas industry, allowing hydrocarbons to be transported over long distances. Maintaining their integrity is critical to environmental preservation, energy security, and community safety. Traditional pipeline assets management has been mainly reactive, addressing faults after they occur, resulting in inefficiencies, safety issues, and increased costs. The challenges are worsened by aging pipeline infrastructure, emphasizing the importance of a proactive approach throughout the pipeline’s life cycle. Life Cycle Cost Analysis-Based Decision Support Systems (LCCA-DSS) provide a novel solution that combines advanced data analytics, risk assessment, and optimization algorithms. By taking into consideration the cost of construction, operation, maintenance, and decommissioning, these systems enable proactive decision-making. A bibliometric review using Elsevier’s Scopus and Web of Science databases found extensive research activities on DSS with 127,719 and 14,450 documents identified respectively. Similarly, and LCCA has 3,951 documents in Scopus and 2,128 in web of science. However, only 77 documents in Scopus and 5 Web of science addressed the integration of LCCA and DSS. Regarding DSS and pipeline integrity management, 29 documents were found in Scopus, while none in Web of science. Likewise, integration of LCCA and pipeline integrity management revealed only one document in Scopus and none in web of science at the time the data was collected. Indicating a limited research effort in this domain. The Study reveal that North America, Europe and Asia are the main contributors, with the United State leading with 19 contributions, followed by Canada with 14, and China with 10, while South America and Africa are the regions that shows minimal research activity in this field. By integrating LCCA-based DSS into reality, pipeline asset integrity management will be transformed, and oil and gas infrastructure will have a reliable, economical, and sustainable future. Based on this, a comprehensive LCCA-based DSS framework was developed, it is anticipated that the implementation of this framework can increase pipeline management effectiveness, lower costs, and improve safety by addressing technical, financial, and operational challenges. Moreover, more research is required, since this study highlights the gaps in the current body of knowledge.
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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 | 0.018 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.086 | 0.119 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".