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不确定性地基水荷载的智能识别初探

ISSN号：1009-640X
期刊名称：水利水运工程学报
时间：2013.2.2
页码：1-6
分类：TV698[水利工程—水利水电工程]
作者机构：[1]三峡大学水利与环境学院,湖北宜昌443002, [2]中国科学院武汉岩土力学研究所,岩土力学与工程国家重点实验室,湖北武汉430071, [3]南京水利科学研究院,江苏南京210029
相关基金：国家杰出青年基金（50925933）;国家自然科学基金（50909066,51209124）;湖北省教育厅科学技术项目（D20101207）
相关项目：边坡稳定性分析的控制论方法

关键词：混凝土坝, 地基水荷载, 不确定性, 智能识别, concrete dam , foundation water load , uncertainty, intelligent identification

中文摘要：

实际地基水荷载存在不确定性,地基水荷载作用方式不同,引起的效应量差异较大,如果人为地将地基水荷载作为面荷载或作为稳定渗流体荷载进行数值计算,参与优化反分析,反演获得的参数值得商榷.将监测点相对位移作为输入,坝体混凝土、岩基材料参数和坝基面一定深度测点水头作为输出,建立了不确定性地基水荷载识别神经网络模型,采用均匀设计原理进行材料参数组合,采用饱和地基非稳定渗流分析获得不同渗流体荷载分布,获得样本进行学习,以此训练好的网络模型描述大坝混凝土、岩基材料参数及地基水荷载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模型,可自动识别出大坝混凝土和岩基的材料参数以及地基水荷载.算例分析表明,本文建立的不确定性地基水荷载识别神经网络模型是可行的.

英文摘要：

There is a great difference in effects caused by uncertainties in the actual foundation water load and in different ways. If the foundation water load is put artificially as a surface load or as a stable seepage body load through numerical calculation in the optimization back analysis, the parameters obtained from inversion are debatable. In this paper, a relative displacement of monitoring stations is taken as an input, and the darn concrete, batholith material parameters and the water head of the face of the dam foundation at a certain depth point are used as an output, thus the identification neural network model for the uncertainty of foundation water load is established. By adopting the uniform design principle of material parameters combination, the saturated foundation non-stationary seepage analysis is made to get different seepage body load distributions, and samples to learn, so as to train a good network in describing the nonlinear relationships of the concrete dam, material parameters, ground water load and dam deformation. The water pressure component separated out from the measured displacements is put into the trained network, which can automatically recognize the dam concrete and batholith material parameters as well as the foundation water load. The calculation examples show that the establishment of a neural network model for the identification of the uncertainty of the foundation water load is feasible.

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