Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ASCE Inspire 2023 |
| Untertitel | Infrastructure Innovation and Adaptation for a Sustainable and Resilient World - Selected Papers from ASCE Inspire 2023 |
| Herausgeber/-innen | Bilal M. Ayyub |
| Herausgeber (Verlag) | American Society of Civil Engineers (ASCE) |
| Seiten | 673-682 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9780784485163 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World - Arlington, USA / Vereinigte Staaten Dauer: 16 Nov. 2023 → 18 Nov. 2023 |
Abstract
This paper focuses on network resilience-based design, which involves carrying out a priori analysis during the design phase to optimize the topology of the current network to increase its resilience against disruptive events. However, solving this problem is computationally expensive as it requires searching through numerous topology structures and conducting multiple resilience analyses under different network topologies, which can become unaffordable for large-scale networks. To address this issue, we propose a new graph neural network that incorporates response flow characteristics to capture additional network features. This neural network is designed to deal with complex problems with high dimensions and nonlinear characterization, and it is integrated into an adaptive framework that combines it with a probabilistic solution discovery algorithm to solve network resilience design problems accurately and efficiently.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Werkstoffmechanik
- Ingenieurwesen (insg.)
- Bauwesen
- Ingenieurwesen (insg.)
- Architektur
Zitieren
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- Apa
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- BibTex
- RIS
ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World - Selected Papers from ASCE Inspire 2023. Hrsg. / Bilal M. Ayyub. American Society of Civil Engineers (ASCE), 2023. S. 673-682.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Machine Learning Assisted Network Resilience Design
AU - Shi, Yan
AU - Beer, Michael
PY - 2023
Y1 - 2023
N2 - This paper focuses on network resilience-based design, which involves carrying out a priori analysis during the design phase to optimize the topology of the current network to increase its resilience against disruptive events. However, solving this problem is computationally expensive as it requires searching through numerous topology structures and conducting multiple resilience analyses under different network topologies, which can become unaffordable for large-scale networks. To address this issue, we propose a new graph neural network that incorporates response flow characteristics to capture additional network features. This neural network is designed to deal with complex problems with high dimensions and nonlinear characterization, and it is integrated into an adaptive framework that combines it with a probabilistic solution discovery algorithm to solve network resilience design problems accurately and efficiently.
AB - This paper focuses on network resilience-based design, which involves carrying out a priori analysis during the design phase to optimize the topology of the current network to increase its resilience against disruptive events. However, solving this problem is computationally expensive as it requires searching through numerous topology structures and conducting multiple resilience analyses under different network topologies, which can become unaffordable for large-scale networks. To address this issue, we propose a new graph neural network that incorporates response flow characteristics to capture additional network features. This neural network is designed to deal with complex problems with high dimensions and nonlinear characterization, and it is integrated into an adaptive framework that combines it with a probabilistic solution discovery algorithm to solve network resilience design problems accurately and efficiently.
UR - http://www.scopus.com/inward/record.url?scp=85179845105&partnerID=8YFLogxK
U2 - 10.1061/9780784485163.079
DO - 10.1061/9780784485163.079
M3 - Conference contribution
AN - SCOPUS:85179845105
SP - 673
EP - 682
BT - ASCE Inspire 2023
A2 - Ayyub, Bilal M.
PB - American Society of Civil Engineers (ASCE)
T2 - ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World
Y2 - 16 November 2023 through 18 November 2023
ER -