ArticleScientific reports2025
Research on the connectivity reliability analysis and optimization of natural gas pipeline network based on topology.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Availability of drone mission with binary decision diagram based on uncertain data.Scientific reports · 2026Article
- Optimized seismic risk mitigation in pipeline routing using a metaheuristic GIS based approach.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The rapid expansion of natural gas pipeline networks in China necessitates robust reliability assessment and optimization frameworks, particularly for large-scale looped configurations where traditional tree-based models fall short. This study proposes an integrated framework combining connectivity reliability evaluation with adaptive topology optimization. First, a minimum path set-based reliability model is developed, leveraging an enhanced depth-first search (DFS) algorithm for efficient path identification and binary decision diagrams (BDD) to eliminate 92% of redundant terms in reliability formulas, reducing computational complexity by 40% compared to Monte Carlo simulations. Second, an adaptive genetic algorithm (AGA) is designed to optimize network topology, dynamically adjusting crossover and mutation rates (0.8≤[Formula: see text]≤0.01, 0.01≤ [Formula: see text]≤ 0.8) based on population diversity, while enforcing constraints through penalty functions (node degree [Formula: see text]=4, pipeline length [Formula: see text]=120 km). Case studies on a regional pipeline network (89 nodes, 98 segments) demonstrate that loop structures exhibit 25.7% higher average reliability ([Formula: see text]= 0.87792) than branch nodes (v79: [Formula: see text]=0.60933). The AGA-driven optimization increases system-wide connectivity reliability ([Formula: see text]) from 0.03 to 0.247 by strategically adding redundant pipelines (v71-v77), outperforming particle swarm optimization (PSO) by 65%. Key findings reveal that centralized gas source layouts and looped configurations significantly enhance redundancy, with critical segments showing 34% higher D-connectivity importance post-optimization. This work provides a scalable, training-free solution for pipeline network design, balancing computational efficiency (68.7s for 200-node networks) with engineering constraints, and offers actionable insights for infrastructure resilience enhancement.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.