Application of an Improved Spatial Correlation Coefficient to Exterior Deformation Monitoring of High Slope in Reservoir Area

HU Tian-yi, YOU Meng-tao, LU Tian-lin, WANG Cheng, DONG An-yu

Journal of Changjiang River Scientific Research Institute ›› 2017, Vol. 34 ›› Issue (7) : 41-47.

PDF(1934 KB)
PDF(1934 KB)
Journal of Changjiang River Scientific Research Institute ›› 2017, Vol. 34 ›› Issue (7) : 41-47. DOI: 10.11988/ckyyb.20160273

Application of an Improved Spatial Correlation Coefficient to Exterior Deformation Monitoring of High Slope in Reservoir Area

  • HU Tian-yi1,2, YOU Meng-tao2, LU Tian-lin3, WANG Cheng1, DONG An-yu1
Author information +
History +

Abstract

Conventional monitoring of reservoir slope is mainly focused on the deformation and stress changes of single point rather than the overall deformation with multiple points. In view of this, an improved spatial correlation coefficient is proposed to calculate the spatial correlations among multiple points of slope. The present coefficient is based on Pearson’s correlation coefficient and Moran’s correlation coefficient, and takes the spatial coordinates of different measuring points into consideration. Three relevance indexes of exterior deformation of slope are proposed, namely relevance degree Rij of measuring point, influence degree Ii of measuring point, and integrity degree I of slope. The overall state of slope can be obtained according to the variation trends of the indexes. The present coefficient is applied to analyzing the deformation data of the high slope of a concrete dam, and the results prove that the coefficient presented in this paper is practical and rational for analyzing the safety of slopes.

Key words

high slope / exterior deformation monitoring / Pearson’s correlation coefficient / Moran’s correlation coefficient / spatial correlation coefficient

Cite this article

Download Citations
HU Tian-yi, YOU Meng-tao, LU Tian-lin, WANG Cheng, DONG An-yu. Application of an Improved Spatial Correlation Coefficient to Exterior Deformation Monitoring of High Slope in Reservoir Area[J]. Journal of Changjiang River Scientific Research Institute. 2017, 34(7): 41-47 https://doi.org/10.11988/ckyyb.20160273

References

[1] 温廷新, 张 波. 露天煤矿边坡稳定性的随机森林预测模型[J]. 科技导报, 2014, 32(4/5): 105-109.
[2] SHARMA R K, MEHTA B S, JAMWAL C S. Cut Slope Stability Evaluation of NH-21 along Nalayan-Gambhrola Section, Bilaspur District, Himachal Pradesh, India[J]. Natural Hazards, 2013, 66(2): 249-270.
[3] 张 豪, 罗亦泳. 基于人工免疫算法的边坡稳定性预测模型[J]. 煤炭学报, 2012, 37(6): 911-917.
[4] 张均锋,丁 烨.边坡稳定性分析的三维极限平衡法及应用[J].岩石力学与工程学报,2005,24(3):365-370.
[5] CHEN Z, WANG X, HABERFIELD C, et al. A Three-dimensional Slope Stability Analysis Method Using the Upper Bound Theorem Part I:Theory and Methods[J]. International Journal of Rock Mechanics & Mining Sciences, 2001, 38(3): 369-378.
[6] 刘寒冰, 李国恒, 谭国金, 等. 基于时间序列的边坡变形实时预测方法[J]. 吉林大学学报(工学版), 2012,42(增1): 193-197.
[7] 王艳霞. 模糊数学在边坡稳定分析中的应用[J]. 岩土力学, 2010, 31(9): 3000-3004.
[8] 向超文,徐锦洪,李 焜,等.人工神经网络边坡稳定预报模型[J].工程地质计算机应用,2006,(1):1-9.
[9] 杜 岩, 谢谟文, 吕夫侠, 等. 基于模态参量变化的边坡动态稳定分析新方法[J].岩土工程学报, 2015, 37(7): 1334-1339.
[10]李南生, 唐 博, 谈风婕, 等. 基于统一强度理论的土石坝边坡稳定分析遗传算法[J].岩土力学, 2013, 34(1): 243-249.
[11]王海军, 涂 凯, 闫晓荣. 基于果蝇优化算法的GRNN模型在边坡稳定预测中的应用[J].水电能源科学, 2015, 33(1): 124-126.
[12]刘大有, 陈慧灵, 齐 红, 等. 时空数据挖掘研究进展[J]. 计算机研究与发展, 2013, 50(2): 225-239.
[13]GAO T. Regional Industrial Growth: Evidence from Chinese Industries[J]. Regional Science and Urban Economics, 2004, 34(1): 101-124.
[14]ANSELIN L. Spatial Econometrics: Methods and Models[M]. Dordrecht: Kluwer Academic, 1988.
[15]ANSELIN L. Space and Applied Econometrics: Introduction[J]. Regional Science and Urban Economics, 1992, 22(3): 307-316.
[16]宋马林, 王舒鸿, 汝慧萍. 一种新的考虑时间和空间的相关系数及其算例[J].数量经济技术经济研究, 2010, (7): 142-152.
[17]COMBES P P. Economic Structure and Local Growth: France, 1984-1993[J]. Journal of Urban Economics, 2000, 47(3): 329-355.
[18]ODLAND J. Spatial Autocorrelation[M]. London: SAGE Publications, 1988.
PDF(1934 KB)

Accesses

Citation

Detail

Sections
Recommended

/

Baidu
map