RESEARCH ON MULTI-STEP PREDICTION OF DEEP EXCAVATION DEFORMATION BASED ON RECURRENT NEURAL NETWORK

Liu Yongjian; Li Zhangming; Zhang Jianlong

doi:10.13204/j.gyjz200409007

Volume 34 Issue 9

Dec. 2014

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Liu Yongjian, Li Zhangming, Zhang Jianlong. RESEARCH ON MULTI-STEP PREDICTION OF DEEP EXCAVATION DEFORMATION BASED ON RECURRENT NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2004, 34(9): 22-25. doi: 10.13204/j.gyjz200409007

Citation:

Liu Yongjian, Li Zhangming, Zhang Jianlong. RESEARCH ON MULTI-STEP PREDICTION OF DEEP EXCAVATION DEFORMATION BASED ON RECURRENT NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2004, 34(9): 22-25. doi: 10.13204/j.gyjz200409007

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RESEARCH ON MULTI-STEP PREDICTION OF DEEP EXCAVATION DEFORMATION BASED ON RECURRENT NEURAL NETWORK

doi: 10.13204/j.gyjz200409007

1.
Geotechnical Engineering Institute,Guangdong University of Technology Guangzhou 510640

Received Date: 2003-12-25
Publish Date: 2004-09-20

Abstract

Abstract

An artificial neural network is introduced in the light of the complexity, nonlinearity of a deep excavation and the importance of multi-step prediction of its deformation.The defect of multi-step prediction by BP network is analyzed and a multi-step prediction model of an excavation deformation based on recurrent neural networks is also proposed.The reliability and practicability of the multi-step prediction of the excavation deformation by the recurrent neural networks are demonstrated through the multi-step prediction of the deformation of deep excavation in soft soil.It can be widely used for the muti-step prediction in other fields.
- deep excavation,
- recurrent neural networks,
- multi-step prediction,
- deformation