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中文摘要：
坝体的变形能反映建筑物的运行状况，受各种复杂因素影响，坝体变形监测数据是一个不平稳的时间序列．基于传统时间序列不能解决非平稳数据，使用自回归求和滑动平均序列建立模型，结合工程实例进行坝体变形监测数据的拟合与预测，并用BP神经网络进行误差预测，得到最终预测值．经过实际大坝的数据建模检验，建模方法可行，预测结果精度高，在大坝安全监测中具有较好的实用性．
英文摘要：
Affected by various comp time series. Based on the traditional licated factors, dam deformation monitoring data is a nonstationary time-series we can not solve the nonstationary data. Autoregressive integrated moving average （ARIMA） model is used to fit and forecast data. The BP neural network is used to predict error. The dam deformation data fitting analysis and forecasting research show that the ARIMA- ANN model is not only quickly enough to improve the efficiency of the algorithm, but also has a good
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Structure analysis for concrete-faced rockfill dams based on information entropy theory and finite e
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