REVIEW ARTICLE
Research on Grey System Model and Its Application on Displacement Prediction in Tunnel Surrounding Rock
Xiaobo Xiong*, 1, 2, 3
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 514
Last Page: 518
Publisher Id: TOMEJ-8-514
DOI: 10.2174/1874155X01408010514
Article History:
Received Date: 10/09/2014Revision Received Date: 05/11/2014
Acceptance Date: 05/11/2014
Electronic publication date: 24/12/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
In the process of the highway tunnel construction, the stability and the reliability of tunnel rock is an important guarantee to ensure safety construction. Tunnel surrounding rock deformation monitoring is an important method for obtaining information on surrounding rock and controlling tunnel stability in the period of construction. Forecasting deformation of surrounding rock is the key to estimate shoring types, parameter and longtime stability after being commissioned for use. Considering the non-linear characteristic of deformation of the tunnel, the grey system prediction models were proposed. Based on the displacement of Guankouya rock tunnel, grey models of the GM(1, 1) and the Model DGM(2, 1) were established for the tunnel grey forecasting model of the rock tunnel displacement. The calculated results show that two models for tunnel displacement generally were predictable. GM(1, 1) and DGM(2, 1) models are similar to the tunnel displacement development model. Application examples demonstrate that it has extraordinary adaptability to the tunnel displacement forecast and all types of surrounding rock displacement can be predicted better by the grey model and the model has high simulation and prediction accuracy.