A landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data

被引：0
|
作者：
Dai Y. [1 ,2 ]
Dai W. [1 ,2 ]
Yu W. [1 ,2 ]
机构：
[1] Department of Surveying Engineering & Geo-Informatics, Central South University, Changsha
[2] Key Laboratory of Precise Engineering Surveying & Deformation Disaster Monitoring of Hunan Province, Changsha
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2022年 / 51卷 / 10期
基金：
中国国家自然科学基金;
关键词：
landslide deformation monitoring; multi-objective optimization problem; robust Helmert variance component estimation; weighted displacement back analysis model;
D O I：
10.11947/j.AGCS.2022.20210163
中图分类号：
学科分类号：
摘要：
In view of the multi-objective optimization problem of landslide parameter inversion, and to compensate for the lack of sparse landslide displacement monitoring point, a landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data is proposed. Firstly, the multi-objective weighted displacement back analysis model is constructed by ground and underground displacement information. Secondly, the robust post-test random model of various observations is calculated by the robust Helmert variance component estimation method, and then it is used to optimize the inversion model. Finally, the equivalent mechanical parameters are solved by iteration computation. Experimental results show that insufficient amount of underground displacement information will lead to serious deviations in the displacement back analysis results, and the inversion results that integrate ground and underground displacement information are more accurate; the multi-objective weighted displacement back analysis method based on robust Helmert variance component estimation can not only reasonably determine the weight of different types of observation data, but also effectively resist the influence of abnormal gross errors on the inversion results, and improve the inversion calculation accuracy. © 2022 SinoMaps Press. All rights reserved.
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收藏
页码：2149 / 2159
页数：10
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