Deformation prediction of layered soft rock tunnel based on convolution neural network

Chen Yang; Tong Yao

doi:10.1117/12.2668198

23 February 2023 Deformation prediction of layered soft rock tunnel based on convolution neural network

Chen Yang, Tong Yao

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 1255107 (2023) https://doi.org/10.1117/12.2668198
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

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.

Citation Download Citation

Chen Yang and Tong Yao "Deformation prediction of layered soft rock tunnel based on convolution neural network", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 1255107 (23 February 2023); https://doi.org/10.1117/12.2668198

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KEYWORDS

Deformation

Convolution

Feature extraction

Convolutional neural networks

Data modeling

Neural networks

Sensors

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