Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

28

Číslo periodika v rámci svazku

December 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001584058100008

EID výsledku v databázi Scopus

2-s2.0-105016311986