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Optimization of Adaptive Active Support Parameters for Large Deformation Prestressed Anchor Ropes in Soft Rock Tunnels Using Genetic Algorithm Based Computer Applications.

Authors :
Wu, Shanshan
Zhang, Ning
Dong, Jicheng
Chen, Li
Source :
Procedia Computer Science; 2024, Vol. 243, p496-505, 10p
Publication Year :
2024

Abstract

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
243
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
180296632
Full Text :
https://doi.org/10.1016/j.procs.2024.09.061