长江科学院院报 ›› 2021, Vol. 38 ›› Issue (12): 82-90.DOI: 10.11988/ckyyb.20200811

• 工程安全与灾害防治 • 上一篇    下一篇

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

陈良捷1, 魏博文1,2, 喻俊豪1, 罗绍杨1, 毛颖1   

  1. 1.南昌大学 建筑工程学院, 南昌 330031;
    2.南京水利科学研究院 水文水资源与水利工程科学国家重点实验室, 南京 210098
  • 收稿日期:2020-08-11 修回日期:2020-11-18 出版日期:2021-12-01 发布日期:2021-12-15
  • 通讯作者: 魏博文(1981-), 男, 江西九江人, 教授, 博士, 主要从事水工结构健康监测与安全控制方面的研究。E-mail: ncuweibowen@126.com
  • 作者简介:陈良捷(1996-), 男, 江西九江人, 硕士研究生, 主要从事水工结构安全监测与数值优化方面的研究。E-mail: 15083823687@163.com
  • 基金资助:
    国家自然科学基金项目(51779115, 51869011); 国家重点实验室开放研究基金项目(2017491511); 江西省研究生创新专项资金资助项目(YC2019-S061)

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

CHEN Liang-jie1, WEI Bo-wen1,2, YU Jun-hao1, LUO Shao-yang1, MAO Ying1   

  1. 1. School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China;
    2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Nanjing HydraulicResearch Institute, Nanjing 210098, China
  • Received:2020-08-11 Revised:2020-11-18 Online:2021-12-01 Published:2021-12-15

摘要: 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。

关键词: 混凝土坝, 位移预报, 多尺度组合机制, 自回归模型体系, NARX, ARIMA

Abstract: 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.

Key words: concrete dam, displacement forecast, multiscale combination mechanism, autoregressive model system, NARX, ARIMA

中图分类号: