基于间接平差的选权迭代法在地铁断面监测中的应用

贺磊; 陈浩; 岁秀珍

doi:10.3973/j.issn.1672-741X.2014.11.004

隧道建设 >

2014 , Vol. 34 >Issue 11: 1036 - 1041

DOI: https://doi.org/10.3973/j.issn.1672-741X.2014.11.004

研究与探索

基于间接平差的选权迭代法在地铁断面监测中的应用

贺磊 ,
陈浩 ,
岁秀珍

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（1.南京市测绘勘测研究院有限公司，江苏南京 210005； 2.义乌市勘测设计研究院，浙江义乌 322000）

贺磊（1982—），男，湖北天门人，2011年毕业于南京工业大学,大地测量学与测量工程专业，硕士，工程师，主要研究方向为精密工程测量。

收稿日期: 2014-05-16

修回日期: 2014-10-06

网络出版日期: 2014-11-20

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Application of Parameteradjustmentbased Selecting Weight Iteration Method in Crosssection Monitoring of Metro Works

HE Lei ,
CHEN Hao ,
SUI Xiuzhen

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(1. Nanjing Institute of Surveying, Mapping & Geotechnical Investigation, Co., Ltd., Nanjing 210005, Jiangsu, China; 2. Yiwu Surveying & Design Institute, Yiwu 322000, Zhejiang, China)

Received date: 2014-05-16

Revised date: 2014-10-06

Online published: 2014-11-20

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摘要

针对地铁隧道断面监测中的断面拟合方法，介绍了常用的最小二乘法和选权迭代法，应用MATLAB对选权迭代法实现算法程序。以南京地铁10号线的监测数据为例，分别对2种拟合法进行比较，结果表明：在无异常值的情况下，最小二乘法与选权迭代法均有较高的拟合精度，随着异常值个数的增加，最小二乘法拟合数据产生失真，且对异常值无法正确定位，而选权迭代法则可以精确定位异常值，对无异常的监测点位有较高的拟合精度。

关键词： 地铁; 断面监测; 断面拟合; 最小二乘; 选权迭代; 异常值

本文引用格式

贺磊 , 陈浩 , 岁秀珍 . 基于间接平差的选权迭代法在地铁断面监测中的应用[J]. 隧道建设, 2014 , 34(11) : 1036 -1041 . DOI: 10.3973/j.issn.1672-741X.2014.11.004

Abstract

Regarding the fitting method in the crosssection monitoring of Metro tunnels, least square method and selecting weight iteration method are presented. The calculation procedures for selecting weight iteration method are realized on basis of MATLAB. The two fitting methods mentioned above are compared on basis of the monitoring data of Line 10 of Nanjing Metro. Conclusions drawn are as follows: Without outliers, Both the least square method and the selecting weight iteration method have high fitting precision; With the increase of the number of outliers, the least squares fitting data become to be distorted and the outliers cannot be found correctly; The outliers can be found correctly by selecting weight iteration method and the monitoring points without outliers have higher fitting precision.

Key words： Metro; crosssection monitoring; crosssection fitting; least square; selecting weight iteration; outliers

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