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Deformation prediction of layered soft rock tunnel based on convolution neural network
In order to improve the reliability of large deformation prediction of layered soft rock tunnel under complex geological conditions, a tunnel large deformation prediction method based on convolution neural network is proposed, which solves the problems of complicated calculation of multiple evaluation indexes' weights and diverse limit values in large deformation prediction. In order to fully consider the influence of layered weak surrounding rock strength, surrounding rock structure type, in-situ stress and groundwater on large deformation of tunnel, six sub-indexes, namely, compressive strength of rock mass, bedding dip angle, initial in-situ stress state, buried depth, corrected quality index of rock mass and groundwater development, are selected to predict the large deformation grade. According to the classification standard of large deformation, a large deformation prediction model based on in-situ stress inversion and on-site large deformation monitoring information is constructed. By using the large deformation information of the tunnel, a convolution neural network large deformation prediction model which accords with the actual law of the target tunnel site is constructed. Convolutional neural network model has high accuracy in predicting sample test sets.
Deformation prediction of layered soft rock tunnel based on convolution neural network
In order to improve the reliability of large deformation prediction of layered soft rock tunnel under complex geological conditions, a tunnel large deformation prediction method based on convolution neural network is proposed, which solves the problems of complicated calculation of multiple evaluation indexes' weights and diverse limit values in large deformation prediction. In order to fully consider the influence of layered weak surrounding rock strength, surrounding rock structure type, in-situ stress and groundwater on large deformation of tunnel, six sub-indexes, namely, compressive strength of rock mass, bedding dip angle, initial in-situ stress state, buried depth, corrected quality index of rock mass and groundwater development, are selected to predict the large deformation grade. According to the classification standard of large deformation, a large deformation prediction model based on in-situ stress inversion and on-site large deformation monitoring information is constructed. By using the large deformation information of the tunnel, a convolution neural network large deformation prediction model which accords with the actual law of the target tunnel site is constructed. Convolutional neural network model has high accuracy in predicting sample test sets.
Deformation prediction of layered soft rock tunnel based on convolution neural network
Yang, Chen (author) / Yao, Tong (author)
Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022) ; 2022 ; Changchun,China
Proc. SPIE ; 12551
2023-02-23
Conference paper
Electronic Resource
English
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