Displacement evolution forecast by genetic-neural network method in underground engineering

The genetic-neural network method was used in forecasting the displacement evolution history in tunnel and underground structure. It is shown that the neural network structure in addition of the genetic-algorithm optimized system can escape from the blindness which is the common phenomenon in man-made choice to the network structure. The neural structure enhances the efficiency of the network study and the capability of the forecasting application. It allows the engineering staff convenient to adjust and optimize the construction step in time by non-linear intelligent recognition to forecast the displace distortion. Measurement to make a contrast analysis with the historical data in order to maintain the stability of the underground structure. The engineering case analyses indicate that there is an extensive prospect for this real time prediction method and the forecasted precision can be continuously better with the accumulating of the samples.