Details
| Original language | English |
|---|---|
| Article number | 111198 |
| Number of pages | 15 |
| Journal | Reliability Engineering and System Safety |
| Volume | 262 |
| Early online date | 1 May 2025 |
| Publication status | E-pub ahead of print - 1 May 2025 |
Abstract
Capacitated network reliability (CNR) analysis is essential for computing the reliability of diverse networks. The NP-hard nature of CNR problems makes exact solutions through exhaustive permutations impractical for many real-world engineering networks. In this research, a new graph-based neural network termed the response flow graph neural network (RFGNN) is developed to address CNR problems. The innovation of the proposed method comprises three key components. Firstly, an iteration equation is proposed to update network link weights by identifying nodes where flow is obstructed during propagation. Secondly, a novel expression is developed to amalgamate local neighborhood information for each node by incorporating the updated link weights, culminating in the creation of the RFGNN. Thirdly, an adaptive framework is developed to improve the prediction accuracy of the RFGNN in solving CNR problems. Several CNR problems are presented to assess the efficacy of the developed method. The results unequivocally demonstrate the effectiveness of the developed method. Furthermore, the RFGNN exhibits remarkable computational accuracy when estimating CNRs across various sub-networks once it is appropriately constructed from the original network. This represents a capability that conventional non-machine learning methods typically struggle to attain.
Keywords
- Adaptive framework, Capacitated network reliability, Complicated networks, Graph-based neural networks, Machine learning
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Reliability Engineering and System Safety, Vol. 262, 111198, 10.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Response flow graph neural network for capacitated network reliability analysis
AU - Shi, Yan
AU - Liu, Cheng
AU - Beer, Michael
AU - Huang, Hong Zhong
AU - Liu, Yu
N1 - Publisher Copyright: © 2025 Elsevier Ltd
PY - 2025/5/1
Y1 - 2025/5/1
N2 - Capacitated network reliability (CNR) analysis is essential for computing the reliability of diverse networks. The NP-hard nature of CNR problems makes exact solutions through exhaustive permutations impractical for many real-world engineering networks. In this research, a new graph-based neural network termed the response flow graph neural network (RFGNN) is developed to address CNR problems. The innovation of the proposed method comprises three key components. Firstly, an iteration equation is proposed to update network link weights by identifying nodes where flow is obstructed during propagation. Secondly, a novel expression is developed to amalgamate local neighborhood information for each node by incorporating the updated link weights, culminating in the creation of the RFGNN. Thirdly, an adaptive framework is developed to improve the prediction accuracy of the RFGNN in solving CNR problems. Several CNR problems are presented to assess the efficacy of the developed method. The results unequivocally demonstrate the effectiveness of the developed method. Furthermore, the RFGNN exhibits remarkable computational accuracy when estimating CNRs across various sub-networks once it is appropriately constructed from the original network. This represents a capability that conventional non-machine learning methods typically struggle to attain.
AB - Capacitated network reliability (CNR) analysis is essential for computing the reliability of diverse networks. The NP-hard nature of CNR problems makes exact solutions through exhaustive permutations impractical for many real-world engineering networks. In this research, a new graph-based neural network termed the response flow graph neural network (RFGNN) is developed to address CNR problems. The innovation of the proposed method comprises three key components. Firstly, an iteration equation is proposed to update network link weights by identifying nodes where flow is obstructed during propagation. Secondly, a novel expression is developed to amalgamate local neighborhood information for each node by incorporating the updated link weights, culminating in the creation of the RFGNN. Thirdly, an adaptive framework is developed to improve the prediction accuracy of the RFGNN in solving CNR problems. Several CNR problems are presented to assess the efficacy of the developed method. The results unequivocally demonstrate the effectiveness of the developed method. Furthermore, the RFGNN exhibits remarkable computational accuracy when estimating CNRs across various sub-networks once it is appropriately constructed from the original network. This represents a capability that conventional non-machine learning methods typically struggle to attain.
KW - Adaptive framework
KW - Capacitated network reliability
KW - Complicated networks
KW - Graph-based neural networks
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=105004261949&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2025.111198
DO - 10.1016/j.ress.2025.111198
M3 - Article
AN - SCOPUS:105004261949
VL - 262
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 111198
ER -