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Dynamic Predication of Tunnel Water Inflow Based on BP Neural Network: A Case Study of the Tongluoshan Tunnel
Abstract On the basis of analyzing the features of tunnel water inflow and precipitation response, a BP neural network model considering the pulse effect, cumulative effect of the precipitation, and integration effect of groundwater system was built using the Tongluoshan tunnel as an example. The simulation results indicate that this model satisfactorily fits the training sample with a mean absolute percent error of 13.27% and has a relative accurate predication accuracy (mean absolute percent error of 15.05%).
Abstract：
On the basis of analyzing the features of tunnel water inflow and precipitation response, a BP neural network model considering the pulse effect, cumulative effect of the precipitation, and integration effect of groundwater system was built using the Tongluoshan tunnel as an example. The simulation results indicate that this model satisfactorily fits the training sample with a mean absolute percent error of 13.27% and has a relative accurate predication accuracy (mean absolute percent error of 15.05%).
.Dynamic Predication of Tunnel Water Inflow Based on BP Neural Network: A Case Study of the Tongluoshan Tunnel[J] MODERN TUNNELLING TECHNOLOGY, 2012,V49(3): 62-66
2012, Vol. 49 