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Structural Engineering and Mechanics Volume 67, Number 2, July25 2018 , pages 105-113 DOI: https://doi.org/10.12989/sem.2018.67.2.105 |
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A new model approach to predict the unloading rock slope displacement behavior based on monitoring data |
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Ting Jiang, Zhenzhong Shen, Meng Yang, Liqun Xu, Lei Gan and Xinbo Cui
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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. | ||
| Key Words | ||
| unloading rock slope; displacement prediction; fuzzy information granulation; genetic algorithm; back propagation neural network | ||
| Address | ||
| Ting Jiang, Zhenzhong Shen, Meng Yang, Liqun Xu and Lei Gan: 1) State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China 2) College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China 3) National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China Xinbo Cui: Information Center of Land and Resources, Binzhou City, Binzhou, China | ||