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A surrogate algorithm for the one-sided tail of structural random nonlinear response
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Wei-hao YIN1, Hai-ting YANG1, Yan-wen HUANG1, Cheng YANG2, Da-gang LÜ3
Journal of Vibration Engineering | 2024, 37(9) : 1485 - 1492
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Journal of Vibration Engineering | 2024, 37(9): 1485-1492
A surrogate algorithm for the one-sided tail of structural random nonlinear response
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Wei-hao YIN1, Hai-ting YANG1, Yan-wen HUANG1, Cheng YANG2, Da-gang LÜ3
Affiliations
  • 1School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China
  • 2National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation,Southwest Jiaotong University, Chengdu 610031,China
  • 3School of Civil Engineering,Harbin Institute of Technology,Harbin 150001,China
Published: 2024-09-28 doi: 10.16385/j.cnki.issn.1004-4523.2024.09.005
Outline
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In the realm of stochastic nonlinear response analysis for large and intricate structures,the Monte Carlo simulation method stands out as a pivotal approach. However,its widespread practicality is hampered by its exorbitant computational costs. To surmount this challenge,researchers have endeavored to develop the active learning-based Gaussian process surrogate model algorithm. Despite its promise in reducing computational expenses,the optimization strategy associated with active learning necessitates further refinement to meet the exacting demands of engineering applications. For this purpose,we introduce a search function endowed with ‘intelligent’ attention capabilities. This function is meticulously crafted to concentrate on exceedingly high-risk one-sided tail events in engineering scenarios. By incorporating this search function,we have engineered an algorithm that surpasses existing methodologies. Our algorithm finds successful application in the analysis of complex adhesive anchoring structures within subway tunnel rings and linings. Compared to conventional methodologies,our algorithm exhibits a remarkable 30% reduction in the estimation error of single-tailed probabilities. This advancement facilitates a more precise estimation of the one-tailed probability distribution governing the stochastic response of complex structures. Consequently,it enhances the precision of assessing the occurrence probability of extreme events. These findings yield invaluable insights for decision-making processes in pertinent engineering domains and insurance sectors.

random vibration  /  nonlinear response  /  Gaussian process surrogate model  /  active learning  /  tail probability
Wei-hao YIN, Hai-ting YANG, Yan-wen HUANG, Cheng YANG, Da-gang LÜ. A surrogate algorithm for the one-sided tail of structural random nonlinear response[J]. Journal of Vibration Engineering, 2024 , 37 (9) : 1485 -1492 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.09.005
Year 2024 volume 37 Issue 9
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2024.09.005
  • Receive Date:2023-09-13
  • Online Date:2026-02-12
  • Published:2024-09-28
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  • Received:2023-09-13
  • Revised:2023-11-06
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Affiliations
    1School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China
    2National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation,Southwest Jiaotong University, Chengdu 610031,China
    3School of Civil Engineering,Harbin Institute of Technology,Harbin 150001,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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