Research On Deformation Prediction Of Deep Excavation By Recurrent Neural Network And Phase Space Reconstruction

With the amelioration of urban rail transit network in Shanghai, projects of the metro construction have showed six trends. That is the foundation pits become deeper, the scale of constructions is getting larger, the distance between buildings and subway is getting closer, the time limit for constructions is more urgent, the geologic feature is more complex, the hidden troubles are more and more. How to accomplish the node target of completing 400km basic network in both 'quick and satisfactory' way under the situation of large scale, leaping over style, high integration of risk becomes a question for the subway constructors in Shanghai. This paper concentrates on the evaluation of safety and deformation prediction of the foundation pits of subway stations in Shanghai soft soil from the needs of real projects.Neural network method is one of the most effective methods of deep foundation pit deformation prediction, which is an intelligent monitor method combined deformation forecast with control. The advantage of neural network lies in it provide a mathematics tool which can study and forecast by itself. In this paper, the Elman neural network is used to intelligently monitor deep foundation excavation. The Elman neural network is a typically dynamic recurrent neural network which is able to learn temporal patterns as well as spatial patterns. Therefore, the trained Elman neural network has the characteristics of the nonlinear and dynamic. At the same time Elman neural network avoids the drawback of traditional neural network which can not change the model structure real time and can not adapt to the abrupt change.The main ideas of the paper are listed as follows:(1) Design monitor system scientifically and put rigorous and effective monitor in practice so as well and truly show all kinds of pulse of structure and environment, and accordingly provide reliably and roundly basic information for design and construction;(2) Based on intelligent monitor and by virtue of the results obtained in this paper, we proposed the basic theory of choosing Elman neural network to model in the deep foundation pit intelligent monitor. Using Elman neural network overcome the disadvantage of slow convergence, easy to fall in to local infinitesimal and not having nonlinear dynamic characteristic when using the Bp neural network and RBF neural network which are largely used in the deep foundation pit deform prediction by far.(3) Change the method of non-continuous and static structure mechanics computation used in the past and make full use of artificial neural network predictive method to dynamically analysis every condition of deep foundation pit construction by time and space sequence and farther obtain the dynamic reliability;(4) Take the practical application into account, in this paper the mulit-data prediction method and time serial prediction method are used to intelligent monitor.