不确定性大坝地基几何尺寸智能识别初探

黄耀英; 郑宏; 向衍; 付学奎

doi:10.3969/j.issn.1001-5485.2013.06.017

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raybet体育在线院报 ›› 2013, Vol. 30 ›› Issue (6) : 76-79. DOI: 10.3969/j.issn.1001-5485.2013.06.017

岩土工程

不确定性大坝地基几何尺寸智能识别初探

黄耀英^1，2a，2b，郑宏^2a，2b，向衍³，付学奎¹

作者信息 +

Preliminary Discussion on Intelligent Identification of Dam Foundation’s Uncertain Geometry Size

HUANG Yao ying ^1,2,3 , ZHENG Hong ^2,3 , XIANG Yan ⁴, FU Xue kui ¹

Author information +

文章历史 +

摘要

大坝地基实际几何尺寸存在不确定性，将监测点相对位移作为输入，坝体混凝土、岩基材料参数和地基几何尺寸作为输出，建立了不确定性地基几何尺寸识别神经网络模型。该模型采用均匀设计原理进行材料参数组合，根据不同地基几何尺寸建立的大坝-地基有限元模型的稳定渗流体荷载分布，获得样本进行学习，以此训练好的网络来描述大坝混凝土、岩基材料参数及地基几何尺寸和坝体变形的非线性关系。将大坝实测位移分离出的水压分量输入训练好的网络，可自动识别出大坝混凝土和岩基的材料参数以及地基几何尺寸。算例分析表明，建立的不确定性大坝地基几何尺寸识别神经网络模型是可行的。

Abstract

The actual geometric size of dam foundation is uncertain. In this research, a neural network model for the intelligent identification of the uncertain geometric size of dam foundation is established. The model takes the relative displacement of monitoring points as input, and the dam concrete, rock foundation material parameters and foundation’s geometric size as output. The load distribution of steady seepage body is obtained, and on the basis of material parameters combined according to uniform design principle, the relative displacement of key monitoring points were calculated as the learning samples. The trained network describes the nonlinear relationship among the dam concrete, rock foundation material parameters and the foundation’s geometric size and dam deformation. The water pressure component separated from the measured dam displacement is input into the trained network to automatically identify the dam concrete and rock foundation material parameters and the foundation’s geometric size. Calculation example shows that this model is feasible.

导出引用

黄耀英，郑宏，向衍，付学奎. 不确定性大坝地基几何尺寸智能识别初探[J]. raybet体育在线院报. 2013, 30(6): 76-79 https://doi.org/10.3969/j.issn.1001-5485.2013.06.017

HUANG Yao ying,ZHENG Hong,XIANG Yan,FU Xue kui. Preliminary Discussion on Intelligent Identification of Dam Foundation’s Uncertain Geometry Size[J]. Journal of Changjiang River Scientific Research Institute. 2013, 30(6): 76-79 https://doi.org/10.3969/j.issn.1001-5485.2013.06.017

中图分类号： TV314，TV642.1

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