Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Parameter Identification of Surrounding Rock in Underground Engineering Based on Complex Function Theory
https://orcid.org/https://orcid.org/0000-0001-8348-1812
https://orcid.org/https://orcid.org/0000-0003-4552-0977
https://orcid.org/https://orcid.org/0000-0003-3203-7679
https://orcid.org/https://orcid.org/0000-0001-9867-211X
To investigate the potential use of the complex function theory in displacement back analysis, a novel method for parameter identification is proposed by combining the complex function theory and the back-propagation neural network optimized by the particle swarm optimization algorithm. The finite element method is replaced with the complex function method to establish a nonlinear relationship between parameters and deformations of the surrounding rock around a complex underground cavern. As demonstrated by a virtual example of an arched tunnel, the deformation parameters were identified by the proposed method and demonstrated to be approximately equal to the predefined parameters. Subsequently, the proposed method is further applied to the inverse analysis of mechanical parameters for the surrounding rock in the underground powerhouse of the Baihetan Hydropower Station. The computed displacements based on the back-analyzed parameters show excellent agreement with the monitoring displacements. The average error between them is about 4.17%, so the proposed method provides the potential to viably enhance the back-analysis technique.
Parameter Identification of Surrounding Rock in Underground Engineering Based on Complex Function Theory
https://orcid.org/https://orcid.org/0000-0001-8348-1812
https://orcid.org/https://orcid.org/0000-0003-4552-0977
https://orcid.org/https://orcid.org/0000-0003-3203-7679
https://orcid.org/https://orcid.org/0000-0001-9867-211X
To investigate the potential use of the complex function theory in displacement back analysis, a novel method for parameter identification is proposed by combining the complex function theory and the back-propagation neural network optimized by the particle swarm optimization algorithm. The finite element method is replaced with the complex function method to establish a nonlinear relationship between parameters and deformations of the surrounding rock around a complex underground cavern. As demonstrated by a virtual example of an arched tunnel, the deformation parameters were identified by the proposed method and demonstrated to be approximately equal to the predefined parameters. Subsequently, the proposed method is further applied to the inverse analysis of mechanical parameters for the surrounding rock in the underground powerhouse of the Baihetan Hydropower Station. The computed displacements based on the back-analyzed parameters show excellent agreement with the monitoring displacements. The average error between them is about 4.17%, so the proposed method provides the potential to viably enhance the back-analysis technique.
Parameter Identification of Surrounding Rock in Underground Engineering Based on Complex Function Theory
https://orcid.org/https://orcid.org/0000-0001-8348-1812
https://orcid.org/https://orcid.org/0000-0003-4552-0977
https://orcid.org/https://orcid.org/0000-0003-3203-7679
https://orcid.org/https://orcid.org/0000-0001-9867-211X
To investigate the potential use of the complex function theory in displacement back analysis, a novel method for parameter identification is proposed by combining the complex function theory and the back-propagation neural network optimized by the particle swarm optimization algorithm. The finite element method is replaced with the complex function method to establish a nonlinear relationship between parameters and deformations of the surrounding rock around a complex underground cavern. As demonstrated by a virtual example of an arched tunnel, the deformation parameters were identified by the proposed method and demonstrated to be approximately equal to the predefined parameters. Subsequently, the proposed method is further applied to the inverse analysis of mechanical parameters for the surrounding rock in the underground powerhouse of the Baihetan Hydropower Station. The computed displacements based on the back-analyzed parameters show excellent agreement with the monitoring displacements. The average error between them is about 4.17%, so the proposed method provides the potential to viably enhance the back-analysis technique.
Parameter Identification of Surrounding Rock in Underground Engineering Based on Complex Function Theory
KSCE J Civ Eng
Yan, Hong-Chuan (Autor:in) / Zhuo, Li (Autor:in) / Shuai, Yong-Jian (Autor:in) / Xie, Hong-Qiang (Autor:in) / Xiao, Ming-Li (Autor:in) / Cai, Ming-Guang (Autor:in)
KSCE Journal of Civil Engineering ; 28 ; 2440-2453
01.06.2024
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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