A deep reinforcement learning method for structural dominant failure modes searching based on self-play strategy
Authors:
- Xiaoshu GuanXGXiaoshu Guan
- Huabin SunHSHuabin Sun
- Rongrong HouRHRongrong Hou
- Yang XuYang Xu
- Yuequan BaoYBYuequan Bao
- Hui LiHLHui Li
- 11 January 2023
- journal article
- research article
- Published by Elsevier in Reliability Engineering & System Safety
- Vol. 233
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (51978216, 52192664, 52208310)
This publication has 26 references indexed in Scilit:
- Mastering the game of Go without human knowledgeNature, 2017
- Seismic reliability assessment of structures using artificial neural networkJournal of Building Engineering, 2017
- Mastering the game of Go with deep neural networks and tree searchNature, 2016
- Human-level control through deep reinforcement learningNature, 2015
- AK-SYS: An adaptation of the AK-MCS method for system reliabilityReliability Engineering & System Safety, 2014
- Zermelo and the Early History of Game TheoryGames and Economic Behavior, 2001
- New directions and research needs in system reliability researchStructural Safety, 1990
- Enumerating significant failure modes of a structural system by using criterion methodsComputers & Structures, 1988
- Automatic generation of stochastically dominant failure modes of frame structuresStructural Safety, 1984
- Reliability of structural systems with correlated elementsApplied Mathematical Modelling, 1982