Adaptive and intelligent prediction of deformation time series of high rock excavation slope
(Institute of Rock and Soil Mechanics; The Chinese Academy of Sciences; Wuhan 430071; P. R. China；
College of Resources and Civil Engineering; Northeastern University; Shenyang 110006; P. R. China；
Yangtze River Scientific Research Institute; Wuhan 430010; P. R. China)
College of Resources and Civil Engineering; Northeastern University; Shenyang 110006; P. R. China；
Yangtze River Scientific Research Institute; Wuhan 430010; P. R. China)
Abstract: Deformation of high rock excavation slope has nonlinear evolution characters. It is very difficult to build mechanical model to describe this nonlinear evoution. A genetic-neural network model has been initially proposed for adaptive and intelligent prediction of deformation of slopes, which used artificial neural network to represent nonlinear evoution of sloPe deformation. Number 0f history points of displacement inputted to the model, topologies of neural network, and learning process of model were adaptive and automatically determined using genetic algorithm. The obtained model was thus optimal at global range, and gave predictions of horizontal displacement at succedent three months for the three measurement points with average relative error of 1. 4 % compared with the measured values. Results from one step prediction and multi-step prediction were combined with the measurements.
Key words: slope; displacement; adaptive; genetic algorithm; neural network