Zobrazeno 1 - 10 of 85 pro vyhledávání: '"Yin-Fu Jin"'
1
Akademický článek
An improved artificial bee colony-random forest (IABC-RF) model for predicting the tunnel deformation due to an adjacent foundation pit excavation
Autor: Tugen Feng, Chaoran Wang, Jian Zhang, Bin Wang, Yin-Fu Jin
Publikováno v: Underground Space, Vol 7, Iss 4, Pp 514-527 (2022)
An improved artificial bee colony-random forest (IABC-RF) model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit. A new search strategy of the artificial bee colony (ABC) algorithm is herein develo
Externí odkaz: https://doaj.org/article/483edf16a78b44498d4f61f40a0d7431
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2
Akademický článek
Three-dimensional numerical analysis of the interaction of two crossing tunnels in soft clay
Autor: Yin-Fu Jin, Bing-Qing Zhu, Zhen-Yu Yin, Dong-Mei Zhang
Publikováno v: Underground Space, Vol 4, Iss 4, Pp 310-327 (2019)
Recently, with the development of underground construction, multi-tunnel engineering has become a matter of concern since the interaction between tunnels at close ranges could cause additional deformation in strata as well as surrounding structures a
Externí odkaz: https://doaj.org/article/45691f6763514fa8857103fc84a9a64e
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3
Damage-healing analysis of microencapsulated self-healing concrete subjected to tensile loading using a 2D micromechanical model
Autor: Kaihang Han, J Woody Ju, Le-Yang Lv, Zhiguo Yan, Xiangsheng Chen, Yin-fu Jin
Publikováno v: International Journal of Damage Mechanics. 32:579-599
Self-healing concrete that employs microencapsulated healing agents has been proven to be an effective method for microcrack repairment in the concrete structure. However, there is a lack of efficient tools to evaluate the effect of the parameters of
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d1a68c17d790c4d69a5ea529e65e8b48
https://doi.org/10.1177/10567895231151726
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6
Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction
Autor: Yin-Fu Jin, Zhen-Yu Yin, Pin Zhang
Publikováno v: Canadian Geotechnical Journal. 59:546-557
This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear fitting capability and uncertainty, which has not previously been used in geotechnical engineering, to propose a modelling strategy in developing prediction model
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6e4247309ad5c900ae680dfcfd6ffb29
https://doi.org/10.1139/cgj-2020-0751
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