Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F25%3A43910012" target="_blank" >RIV/60076658:12510/25:43910012 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S259012302503364X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S259012302503364X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rineng.2025.107309" target="_blank" >10.1016/j.rineng.2025.107309</a>
Alternative languages
Result language
angličtina
Original language name
Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations
Original language description
The use of artificial intelligence (AI) in gas pipeline monitoring and maintenance represents a significant advancement in the energy industry. This article provides an overview of current trends and AI technologies applied in fault detection, failure prediction, and gas transportation optimization. Key methods include machine learning, deep neural networks, numerical simulations, and digital twins. Research highlights the importance of integrating AI with the physical properties of materials for localizing and assessing corrosion defects. A bibliometric analysis reveals that most studies focus on the application of neural networks, support vector machines, and Bayesian networks in predictive maintenance. Despite significant progress, challenges remain, such as the lack of high-quality datasets, high implementation costs, and regulatory barriers. Future research trends focus on the integration of AI with SCADA systems, improving predictive models, and the broader use of generative neural networks for data synthesis. This review of research trends from 2020 to 2025 underscores the importance of artificial intelligence in the transportation sector and highlights its potential for further development in enhancing the reliability and safety of energy infrastructures.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Results in Engineering
ISSN
2590-1230
e-ISSN
2590-1230
Volume of the periodical
28
Issue of the periodical within the volume
December 2025
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
17
Pages from-to
1-17
UT code for WoS article
001584058100008
EID of the result in the Scopus database
2-s2.0-105016311986