Abstract：
Predicting surrounding rock deformation in fault zones has been much debated in observation-based tunnel construction and management, but so far a scientific and rational method for this sort of prediction is lacking. For NATM tunnelling, surrounding rock deformation is often used as an important indicator to judge the stability of a tunnel and the economic rationality of the supporting structure. As surrounding rock deformation is a kind of series varying with time, a prediction model is established to trace and predict deformation in real time. Considering the large deformation rate of the surrounding rock around the Daxiangling Tunnel on the Ya'an-Xichang expressway, a BP artificial neural network based genetic algorithm is introduced, improving prediction accuracy by modifying the basic genetic algorithm. Using GA-BP neural network techniques, a comprehensive model to predict rock deformation in a fractured fault zone is established and applied to the Daxiangling tunnel, and the predicted results are verified to be accurate and reliable.
ZHANG Zhi-Qiang,
LI Hua-Yun,
HAN etc
.Prediction of Surrounding Rock Deformation of the Daxiangling Tunnel
in Fault Zones Using the GA-BP Nerve Network Technique
[J] MODERN TUNNELLING TECHNOLOGY, 2014,V51(2): 83-89
2014, Vol. 51 