An improved SCGM(1,m) model for multi-point deformation analysis
An improved SCGM(1,m) model for multi-point deformation analysis
复制标题
一种改进的多点变形分析SCGM(1,m)模型
DOI：
10.1007/s12303-014-0012-z
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发表时间：
2014-04
影响因子：
1.2
通讯作者：
ZHU Jian-jun
中科院分区：
文献类型：
--
作者：
Wang Qi-jie;WANG Chang-sheng;XIE rong-an;ZHANG Xin-qing;ZHU Jian-jun
考虑到同一变形体内离散监测点的变形往往具有相似的物理性质，且往往经历相同的动力学过程，在时域内对这些点的变形过程进行联合建模有望产生更好的结果。Yin等（1997）首次将建模机理明显优于单变量灰色模型的多变量灰色模型--系统云灰色模型SCGM（1，m）推广到多点变形建模。然而，该模型尚未得到广泛的认可，其应用仍然非常有限的变形分析领域。本研究的目的是证明SCGM（1，m）模型的能力，提出两个修订，以进一步提高模型的性能，并提请更多的关注变形分析的社会。本文首先介绍了SCGM（1，m）模型在形变观测分析和预测中的基本原理。提出了两种改进SCGM（1，m）模型的实用技术，即残差模型重构和线性回归调整。结合边坡监测数据，对SCGM（1，m）模型进行了残差重建模和线性回归调整，并对改进后的模型进行了建模。平均相对预测误差从5.89%分别下降到3.54%和2.69%，表明两种改进技术分别提高了39.9%和54.3%。
Considering the deformation of discrete monitoring points within the same deformable body usually have similar physical properties and tend to undergoing identical dynamic process, joint modelling of the deformation processes of these points in time domain are expected to generate better results. Yin et al. (1997) first extended the multi-variable grey model-system cloud grey model SCGM(1,m), with obviously superior modelling mechanism than single-variable grey model, to multi-point deformation modelling. However, this model is still not widely recognized and its applications remain very limited in the field of deformation analysis. The objective of this study is to demonstrate the capability of the SCGM(1,m) model, to present two revisions to further improve the performance of the model and to draw more attention to the community of deformation analysis. We first introduce the principles of the SCGM(1,m) model in the analysis and prediction of deformation surveys. Two practical techniques, namely residuals re-modelling and linear regression adjustment, are then presented to improve the SCGM(1,m) model. Combined with slope monitoring data, the modelling with the original and the improved SCGM(1,m) models by residuals re-modelling and linear regression adjustment are illustrated. The mean relative prediction errors decrease from 5.89% to 3.54% and 2.69%, when the two refining techniques are applied, respectively, indicating relative improvements of 39.9% and 54.3%.
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DOI：
10.1109/isuma.1990.151299
发表时间：
1990-12
期刊：
[1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis
影响因子：
--
作者：
Mian-Yun Chen
通讯作者：
Mian-Yun Chen
影响因子：
1.6
作者：
Chen Qi;A. Chrzanowski
通讯作者：
Chen Qi;A. Chrzanowski
影响因子：
3
作者：
Jin, Shuanggen;Park, Pil-Ho
通讯作者：
Park, Pil-Ho
影响因子：
5.3
作者：
Shuanggen Jin;P. Park;Wen-yao Zhu
通讯作者：
Shuanggen Jin;P. Park;Wen-yao Zhu
DOI：
--
发表时间：
2001
期刊：
--
影响因子：
--
作者：
K. Hudnut;Y. Bock;J. Galetzka;F. Webb;W. Young
通讯作者：
K. Hudnut;Y. Bock;J. Galetzka;F. Webb;W. Young