›› 2015, Vol. 29 ›› Issue (4): 61-64.

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Deep Excavation Deflection Prediction Using on BP and ARX Models

LI Shangqin, LUO Wuzhang, CHEN Xiongtu, TANG Fengqiang   

  1. (College of Water Conservancy and Civil Engineering, South China Agricultural University,Guangzhou 510642)
  • Received:2014-07-23 Online:2015-08-20 Published:2015-11-06

Abstract: With the development of data collection, transfer and the requirement of safety in the construction, the deflection monitoring of the deep excavation becomes an integral part of the design as well as construction. The neural network approach through Back Propagation (BP) model and Auto Regressive with eXtra Input Signal (ARX) model has been applied in the deep excavation deflection monitoring and prediction. The results indicate that both models can satisfactorily predict the construction deflections. However, from the mean square error point view, BP has a better prediction results compared with the predictions made by ARX model.

Key words: Deep Excavation Deflection Prediction, Back Propagation (BP) Model, Auto Regressive with eXtra Input Signal (ARX) Model, Learning Efficiency, Result Efficiency, Result Comparison