Study on prediction model of deep pit deformation

Author

Guo, Jian ; Dong, E.

fYear

2011

fDate

22-24 April 2011

Firstpage

1635

Lastpage

1638

Abstract

Elman neural network (ENN) is one of the well-known dynamic recurrent neural networks. A new self-adaptive particle swarm optimization (SPSO) was proposed to improve Elman in order to solve problems of dynamic prediction in this paper. SPSO combines ENN and form SPSONN hybrid algorithm. Based on the algorithm, a nonlinear time-varying model was established to prediction deformation of deep foundation pit. The results of an engineering case indicate that the intelligent prediction model is efficiencies in the complex underground structures.

Keywords

foundations; particle swarm optimisation; recurrent neural nets; structural engineering computing; time-varying systems; ENN; Elman neural network; SPSONN hybrid algorithm; complex underground structures; deep pit deformation; dynamic recurrent neural networks; nonlinear time-varying model; prediction model; self-adaptive particle swarm optimization; Algorithm design and analysis; Artificial neural networks; Heuristic algorithms; Monitoring; Numerical models; Prediction algorithms; Predictive models; Elman neural network; deep foundation pit; hybrid algorithm; time-varying model;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Technology and Civil Engineering (ICETCE), 2011 International Conference on

Conference_Location

Lushan

Print_ISBN

978-1-4577-0289-1

Type

conf

DOI

10.1109/ICETCE.2011.5775295

Filename

5775295