Risk prediction of water inrush of karst tunnels based on BP neural network

DOI

10.2991/mmme-16.2016.74How to use a DOI?

Keywords

karst tunnel; water inrush; BP neural network; risk prediction; advanced geological prediction

Abstract

To evaluate precisely the risk level of karst tunnel helps reduce the risk of sudden flood water accidents in the process of tunnel construction. On the basis of relevant literature, statistical study and comprehensive analysis of hydrogeological condition in karst tunnel, and select unfavorable geology, formation lithology, under-ground water level, topography and geomorphology, strata dip Angle, fracture of surrounding rock as risk evaluation index of karst tunnel water gushing. In different hydrogeological conditions, varies a lot. Using BP neural network method to analysis water gushing risk of karst tunnel and avoid the weight of factors. In engi-neering applications, assess water risk of tunnel by method of BP neural network, avoid the occurrence of sudden flood water, which provides reference for risk prediction of water gushing in karst tunnel.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhuo Yang
PY  - 2016/10
DA  - 2016/10
TI  - Risk prediction of water inrush of karst tunnels based on BP neural network
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 327
EP  - 330
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.74
DO  - 10.2991/mmme-16.2016.74
ID  - Yang2016/10
ER  -