Study on prediction of chimney foundation settlement of
Yangluo Power Plant in Wuhan

LUO Hong-ming; TANG Hui-ming; YAN E-chuan

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Study on prediction of chimney foundation settlement of Yangluo Power Plant in Wuhan

更新时间：2026-06-25

- Study on prediction of chimney foundation settlement of Yangluo Power Plant in Wuhan
- Vol. 27, Issue S1, (2006)
- 作者机构：
  
  中国地质大学工程学院,武汉,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU451
- Online First：15 December 2006，
  
  Published：2006
- 稿件说明：
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LUO Hong-ming, TANG Hui-ming, YAN E-chuan. Study on prediction of chimney foundation settlement of Yangluo Power Plant in Wuhan[J]. 2006, 27(S1).
DOI：

LUO Hong-ming, TANG Hui-ming, YAN E-chuan. Study on prediction of chimney foundation settlement of Yangluo Power Plant in Wuhan[J]. 2006, 27(S1). DOI：

摘要

由于影响因素的复杂性及参数选取的问题，基于固结理论的沉降计算值与实测值有较大差异，沉降预测有着重要的研究意义。灰色系统理论成功地解决了许多信息不完全的预测问题，并得到了广泛的应用。灰色模型的建立，是以等时距原始数据作为前提条件的。由于在实际应用时所获取的数据往往不是等时距的，难以建立其预测模型。为了克服灰色模型以上缺点，对灰色模型进行了改进

建立了不等时矩的预测模型。将不等时距灰色预测模型应用于武汉阳逻电厂烟囱基础沉降中，并采用FLAC3D有限差分程序对烟囱基础沉降全过程数值模拟，通过跟踪关键监测点的位移与实际监测值对比研究，结果表明，不等时距灰色预测模型精度较好，可以运用到实际工程中。

Abstract

Because of the complexity of influenced factor and the problem of selecting parameters

The bigger difference is consisted in the settlement value calculated and observed

the study on the prediction of settlement has the magnitude significance. The number of prediction problem of inadequacy information is successfully solved by using grey system theory

which is applied widely. The foundation of grey model is based on the equal interval original data. It is difficult to establish prediction model because the equal interval data is not obtained from the practical application. In order to overcome the grey model shortcomings

the grey model is improved. The unequal interval grey model is established and applied to predict the foundation settlement of Yangluo Power Plant chimney in Wuhan. The numerical simulation of the complete process chimney foundation settlement is used by FLAC3D program. The comparison research of the key point displacement by tracking and practical result by monitoring is studied. The result shows that the precision of prediction model is good and can be applied to practice.

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