Establishment of grey system model about tunnel surrounding rock convergence and information renewal GM(1,1) model forecasting

Deformation monitoring of tunnel surrounding rock plays an important role in process of tunnel construction, which is also a important method of grasping surrounding rock dynamic information and ensuring stability in the period of tunnel construction. Prediction of surrounding rock displacement is t...

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Bibliographic Details
Main Authors: Tieding Dai, Deming Xie, Hongzhi Yao, Guofu Li
Format: Conference Proceeding
Language:English
Subjects:
1) model
Convergence
Data models
Deformable models
Forecasting
grey system theory
information renewal GM
Mathematical model
Predictive models
Rocks
tunnel surrounding rock
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Summary:Deformation monitoring of tunnel surrounding rock plays an important role in process of tunnel construction, which is also a important method of grasping surrounding rock dynamic information and ensuring stability in the period of tunnel construction. Prediction of surrounding rock displacement is the key method of changing support parameter design, estimating stability of surrounding rock primary support and forecasting of accident and dangerous situation which may occur. Traditional forecast method is general numerical calculation which is based on geotechnical mechanics and statistical fitting method, or other method. In this paper, according to surrounding rock convergence deformation data about tunnel, programming is carried on which is based on MATLAB7.0 software;GM (1,1) forecasting model of surrounding rock convergence deformation is established, and the deformation of surrounding rock is forecasted. Through comparative study of forecasting value and measured value of two tunnel sections, it is found that GM(1,1) model is suitable for short term forecasting, and about 2-3 data after giving date could be forecasted. Precision of information renewal GM(1,1) model is higher than which of normal GM(1,1)model, and information renewal GM(1,1) model is suitable for long term forecasting.
DOI:10.1109/RSETE.2011.5964276