Multi-objective optimization-based updating of predictions during excavation

In this paper, an efficient multi-objective optimization (MOOP)-based updating framework is established, which involves (1) the development of an enhanced multi-objective differential evolution algorithm with good searching ability and high convergence speed, (2) the development of an enhanced aniso...

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Published in:Engineering applications of artificial intelligence Vol. 78; pp. 102 - 123
Main Authors: Jin, Yin-Fu, Yin, Zhen-Yu, Zhou, Wan-Huan, Huang, Hong-Wei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2019
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ISSN:0952-1976, 1873-6769
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Abstract In this paper, an efficient multi-objective optimization (MOOP)-based updating framework is established, which involves (1) the development of an enhanced multi-objective differential evolution algorithm with good searching ability and high convergence speed, (2) the development of an enhanced anisotropic elastoplastic model considering small-strain stiffness with its implementation into a finite element code, and (3) the proposal of an identification procedure for parameters using field measurements followed by an updating procedure. The proposed updating framework is verified with a well-documented excavation case where the small-strain stiffness, the anisotropy of elasticity, the anisotropy of yield surface for natural clays, and the parameters of the supporting structures and diaphragm wall are consecutively updated during the staged excavation process. The advantages of the proposed updating framework compared to the Bayesian updating on the same case are also illustrated.
AbstractList In this paper, an efficient multi-objective optimization (MOOP)-based updating framework is established, which involves (1) the development of an enhanced multi-objective differential evolution algorithm with good searching ability and high convergence speed, (2) the development of an enhanced anisotropic elastoplastic model considering small-strain stiffness with its implementation into a finite element code, and (3) the proposal of an identification procedure for parameters using field measurements followed by an updating procedure. The proposed updating framework is verified with a well-documented excavation case where the small-strain stiffness, the anisotropy of elasticity, the anisotropy of yield surface for natural clays, and the parameters of the supporting structures and diaphragm wall are consecutively updated during the staged excavation process. The advantages of the proposed updating framework compared to the Bayesian updating on the same case are also illustrated.
Author Yin, Zhen-Yu
Huang, Hong-Wei
Zhou, Wan-Huan
Jin, Yin-Fu
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  givenname: Zhen-Yu
  surname: Yin
  fullname: Yin, Zhen-Yu
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  givenname: Wan-Huan
  surname: Zhou
  fullname: Zhou, Wan-Huan
  organization: Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Macau, China
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  givenname: Hong-Wei
  surname: Huang
  fullname: Huang, Hong-Wei
  organization: Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China
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Keywords Finite element method
Clay
Excavation
Constitutive model
Automatic updating
Multi-objective optimization
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Snippet In this paper, an efficient multi-objective optimization (MOOP)-based updating framework is established, which involves (1) the development of an enhanced...
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SubjectTerms Automatic updating
Clay
Constitutive model
Excavation
Finite element method
Multi-objective optimization
Title Multi-objective optimization-based updating of predictions during excavation
URI https://dx.doi.org/10.1016/j.engappai.2018.11.002
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