Structural Engineering and Mechanics

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A new model approach to predict the unloading rock slope displacement behavior based on monitoring data

  • Jiang, Ting (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Shen, Zhenzhong (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Yang, Meng (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Xu, Liqun (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Gan, Lei (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Cui, Xinbo (Information Center of Land and Resources)
  • Received : 2018.05.09
  • Accepted : 2018.06.21
  • Published : 2018.07.25
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Abstract

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

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