DEEP EXCAVATION MULTI-POINT DISPLACEMENT MONITORING MODEL AND DETERMINATION OF RBF CENTER BY FCM

Huang Ming; Liu Jun

doi:10.13204/j.gyjz201203017

Volume 42 Issue 3

Dec. 2014

Turn off MathJax

Article Contents

Article Navigation > INDUSTRIAL CONSTRUCTION > 2012 > 42(3): 80-83,158

Huang Ming, Liu Jun. DEEP EXCAVATION MULTI-POINT DISPLACEMENT MONITORING MODEL AND DETERMINATION OF RBF CENTER BY FCM[J]. INDUSTRIAL CONSTRUCTION, 2012, 42(3): 80-83,158. doi: 10.13204/j.gyjz201203017

Citation:

Huang Ming, Liu Jun. DEEP EXCAVATION MULTI-POINT DISPLACEMENT MONITORING MODEL AND DETERMINATION OF RBF CENTER BY FCM[J]. INDUSTRIAL CONSTRUCTION, 2012, 42(3): 80-83,158. doi: 10.13204/j.gyjz201203017

Citation:

PDF( 0 KB)

DEEP EXCAVATION MULTI-POINT DISPLACEMENT MONITORING MODEL AND DETERMINATION OF RBF CENTER BY FCM

doi: 10.13204/j.gyjz201203017

Huang Ming¹,
Liu Jun²

1.
1. School of Civil and Hydraulic Engineering,Hefei University of Technology,Hefei 230009,China;
2.
2. School of Naval Architecture,Ocean and Civil Engineering,Shanghai Jiaotong University,Shanghai 200240,China

Received Date: 2011-10-12
Publish Date: 2012-03-20

Abstract

Abstract

To establish reasonable deep excavation multi-point displacement monitoring model,radial basis function artificial neural network(RBF)was taken as frame.Its input layer came from displacement mechanical theory,and output layer was formed by interrelated multi-point displacement.Considering excavation and displacement characteristics,special preselecting RBF centers and Fuzzy C-means Algorithm(FCM)were used together to confirm RBF centers.Instances showed that these methods were more reasonable and possed good training and forecasting results.
- multi-point,
- deep excavation,
- displacement monitoring model,
- preselecting RBF centers,
- FCM