Multi-objective optimization of tunnel displacement control construction parameters based on nonlinearity

DOI

10.2991/978-94-6463-404-4_37How to use a DOI?

Keywords

tunnel engineering; support vector machine; variable rotation method; displacement prediction

Abstract

In tunnel engineering, displacement and deformation monitoring is an important link, but monitoring data often presents complex nonlinear characteristics. Taking the construction of a highway tunnel as the background, based on the support vector machine algorithm, the variable rotation method is used to optimize its parameters, presenting the complex nonlinear characteristics of monitoring data and establishing a nonlinear model of tunnel displacement monitoring time series. Using this model to make accurate predictions of future displacement and deformation, scientifically guiding on-site monitoring and construction. The experimental results show that after constructing 15 time series as learning samples, rolling prediction is performed using 15 data as prediction samples. As can be seen, the maximum relative error of the prediction is 0.365%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fengfeng Guo
PY  - 2024
DA  - 2024/04/29
TI  - Multi-objective optimization of tunnel displacement control construction parameters based on nonlinearity
BT  - Proceedings of the 2024 3rd International Conference on Structural Seismic Resistance, Monitoring and Detection (SSRMD 2024)
PB  - Atlantis Press
SP  - 377
EP  - 383
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-404-4_37
DO  - 10.2991/978-94-6463-404-4_37
ID  - Guo2024
ER  -