Yin-Fu Jin

Prof. Yin-Fu Jin


Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 July 2017 in International Journal for Numerical and Analytical Methods in Geomechanics
Verified
Journal Article
Closed access
PDF file
Zhen-Yu YinZhen-Yu Yin
Zhen-Yu Yin
Yin-Fu JinYin-Fu Jin
Yin-Fu Jin
JSJack Shuilong Shen
Jack Shuilong Shen
PHPierre‐Yves Hicher
Pierre‐Yves Hicher

5 May 2022 in International Journal for Numerical and Analytical Methods in Geomechanics
Verified
Journal Article
Closed access
PZPin Zhang
Pin Zhang
Zhen-Yu YinZhen-Yu Yin
Zhen-Yu Yin
Yin-Fu JinYin-Fu Jin
Yin-Fu Jin
BSBrian Sheil
Brian Sheil

27 April 2021 in International Journal for Numerical and Analytical Methods in Geomechanics
Verified
Journal Article
Closed access
PDF file
PZPin Zhang
Pin Zhang
Yin-Fu JinYin-Fu Jin
Yin-Fu Jin
ZYZhen‐Yu Yin
Zhen‐Yu Yin

25 February 2023 in Acta Geotechnica
Verified
Journal Article
Closed access
PDF file
NZNing Zhang
Ning Zhang
Zhou AnnanZhou Annan
Zhou Annan
Yin-Fu JinYin-Fu Jin
Yin-Fu Jin
Zhen-Yu YinZhen-Yu Yin
Zhen-Yu Yin
Shui-Long ShenShui-Long Shen
Shui-Long Shen

16 June 2020 in Journal of Zhejiang University-SCIENCE A
Verified
Journal Article
Closed access
PDF file
Zhen-Yu YinZhen-Yu Yin
Zhen-Yu Yin
Yin-Fu JinYin-Fu Jin
Yin-Fu Jin
Zhongqiang LiuZhongqiang Liu
Zhongqiang Liu

概要

岩土材料的复杂和不确定性致使传统理论在模拟 和预测岩土工程问题经常显得无能为力。近年 来, 随着人工智能和大数据技术的快速发展, 人 工智能技术在岩土工程领域有了广泛应用, 例如 岩土参数的优化智能识别和预测、边坡变形的长 期预测、基坑开挖过程变形的实时监测和预测以 及盾构隧道的变形和盾构机刀盘参数的预测和 更新等。为此, 本专辑收集了在该研究领域具有 代表性的研究成果, 介绍了人工智能技术在岩土 工程领域的进展和未来发展潜力, 希望能帮助读 者快速了解人工智能技术在岩土工程中的应用, 以及推动岩土工程的智能化发展, 为实现岩土工 程智能化提供科学依据和技术支撑。


1 May 2017 in KSCE Journal of Civil Engineering
Verified
Journal Article
Open access
PDF file
Yin-Fu JinYin-Fu Jin
Yin-Fu Jin
Zhen-Yu YinZhen-Yu Yin
Zhen-Yu Yin
YRYvon Riou
Yvon Riou
PHPierre-Yves Hicher
Pierre-Yves Hicher
The paper aims to construct an efficient optimization method for identifying creep and destructuration related governing parameters of soft structured clay. An elastic viscoplastic model has been developed and adopted. Different optimization processes, by genetic algorithm or particle swarm optimization with uniform or random samplings initialization methods, are carried out to obtain the material parameters from conventional undrained triaxial tests performed on a K 0-concolidated natural soft clay. All comparisons demonstrate that the uniqueness of the solution is better guaranteed with the genetic algorithm rather than with the particle swarm optimization method. Furthermore, the efficiency of genetic algorithm has been verified by simulating other tests.