李萌, 包腾飞, 杨建慧, 任杰. 灰色模型改进的大坝变形分形几何监控模型[J]. 水利水运工程学报, 2016, (4): 104-110.
引用本文: 李萌, 包腾飞, 杨建慧, 任杰. 灰色模型改进的大坝变形分形几何监控模型[J]. 水利水运工程学报, 2016, (4): 104-110.
LI Meng, BAO Teng-fei, YANG Jian-hui, REN Jie. Fractal geometry monitoring model for dam deformation based on improved grey model[J]. Hydro-Science and Engineering, 2016, (4): 104-110.
Citation: LI Meng, BAO Teng-fei, YANG Jian-hui, REN Jie. Fractal geometry monitoring model for dam deformation based on improved grey model[J]. Hydro-Science and Engineering, 2016, (4): 104-110.

灰色模型改进的大坝变形分形几何监控模型

Fractal geometry monitoring model for dam deformation based on improved grey model

  • 摘要: 为了对大坝安全进行准确监控,利用分形几何理论预测大坝变形。针对一般常维分形分布不能很好分析大坝变形数据的问题,对监测数据进行N阶累计和变换,对变换后的数据利用分段变维分形模型计算各阶变形维数序列,再选择效果较好分形维数已知序列预测未知分形维数,最后反推大坝变形预测数据。针对传统变维分形预测模型分形维数预测方法的不确定性和所需监测数据量大的缺点,利用灰色模型预测分形维数,建立改进的大坝分形几何监控模型。结合工程实例,对比插值法预测分形参数的传统分形几何预测模型和灰色模型改进后的预测模型之间的预测精度,结果表明,改进分形模型不仅在预测精度上有所提高,而且更具稳定性和抗波动性。

     

    Abstract: In this paper, the fractal geometry theory is used to predict dam deformation, which can help to monitor the dam safety accurately. It is difficult to use a constant dimension fractal model to analyse and forecast the dam deformation data. Therefore, a variable dimension fractal model is applied to analyse dam deformation. Firstly, the monitoring data are taken into a transform of cumulative sum of N order. Then, we calculate fractal dimension sequence of each order and choose the best sequence. Finally, the dam deformation forecasting data are calculated in turn. Considering the problem that the traditional prediction method of the fractal dimension is uncertain and that the amount of required monitoring data is large, the prediction of the fractal dimension is made to establish an improved dam fractal geometry monitoring model by use of a grey model. Analysis results show that, based on practical engineering, by comparing the prediction precision of the traditional fractal geometry monitoring model and the improved model, the prediction accuracy of the improved model is better, and the model has the stability of prediction and the resistance to volatility.

     

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