基于多尺度自回归模型体系的混凝土坝位移预报及工程应用

针对混凝土坝位移监测数据的时频非线性特征严重影响到数值模型预报精度的难题,通过小波技术解析原型数据中多重交叉环境驱动的效应实况,有机结合非线性自回归模型(Nonlinear Autoregressive Model with Exogenous Input, NARX)和差分整合移动平均自回归模型(Autoregressive Integrated Moving Average Model, ARIMA),建立了多尺度组合机制下的自回归模型体系,解决了内蕴复杂混沌特性的监测序列的信息挖掘难点。工程实例分析表明,所建模型的拟合精度及预测能力均得以提升,相比于传统模型具有较好的抗噪性和鲁棒性。此外,所建立的计算模型经一定的优化和拓展,亦可推广应用于其它水工建筑物的效应预报分析。

Concrete Dam Displacement Prediction and Engineering Application Based on Multiscale Autoregressive Model System

The accuracy of numerical model prediction for concrete dam displacement is severely affected by the time-frequency nonlinearity of the displacement monitoring data. In view of this, the wavelet technology is employed to analyze the effect of multiple cross environment driving in the prototype data, and then the nonlinear autoregressive model with exogenous input (NARX) and autoregressive integrated moving average model (ARIMA) are integrated to established an autoregressive model system under multiscale combination mechanism to overcome the difficulty of information mining for monitoring sequences with complex chaotic characteristics. Engineering examples manifest that the present model has better fitting accuracy and predictive ability, as well as noise resistance and robustness than traditional models. In addition, being optimized and extended, the model can also be applied to the prediction of other hydraulic structures.