基于自适应变异粒子群算法改进OGM(1，N)及其在排土场变形预测中的应用

doi:10.13722/j.cnki.jrme.2019.1022

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1353 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Considering the traditional GM(1，1) model in slope deformation prediction can not reflect the impact of external environmental changes on the trend of system change and inherent structural shortcomings in GM(1，N) model，OGM(1，N) model was introduced and its background value is optimized by adaptive particle mutation swarm optimization according to the error of background value in gray system. Three monitoring points(G7，G8，G9) on the waste dump were analyzed by grey relational analysis theory. Then the improved OGM(1，N)model was used to predict the waste dump deformation in Jiepailin mine. Compared the results with GM(1，1) model，Verhulst model and Nonlinear regression，the results of three monitoring points showed that the improved OGM(1，N) model is more precise than the other three models. In detail，the average relative percentage error of improved OGM(1，N) model is 0.16%，2.26%，10.562%，respectively. The average relative percentage error of GM(1，1) model is 16.57%，18.07%，19.095%，respectively；the average relative percentage error of Verhulst model is 4.52%，2.34%，29.809%，respectively，the average relative percentage error of nonlinear model is 11.44%，8.45%，11.621%，respectively. Comparing the result of the OGM(1，N) model before and after optimization，the average relative simulation percentage error after optimization was reduced to about more than 1/3 of that before optimization. According to the prediction results of three monitoring points，the improved OGM(1，N) model is adaptive and effective in slope deformation prediction.

Key words： slope engineering；slope deformation prediction；gray prediction；OGM(1 N)；adaptive particle mutation swarm optimization；multivariate prediction model

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	TANG Chao1
	CHEN Yanyin1
	LI Shulin1
	LIU Yinchi1
	HU Jingyu2
	PENG Fuhua2

Cite this article:

TANG Chao1,CHEN Yanyin1,LI Shulin1, et al. The application of improved OGM(1，N) in waste dump deformation prediction based on adaptive particle mutation swarm optimization[J]. , 2020, 39(S1): 3197-3205.

URL:

http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.1022 OR http://rockmech.whrsm.ac.cn/EN/Y2020/V39/IS1/3197

[7]	LIN Y H，LEE P C. Novel high-precision grey forecasting model[J]. Automation in Construction，2007，16(6)：771–777.
[5]	何习平，华锡生，何凤秀. 加权多点灰色模型在高边坡变形预测中的应用[J]. 岩土力学，2007，28(6)：1 187–1 191.(HE Xiping，HUA Xisheng，HE Fengxiu. Weighted multi-point grey model and its application to high rock slope deformation forecast[J]. Rock and Soil Mechanics，2007，28(6)：1 187–1 191.(in Chinese))
[9]	陈妍颖. 露天矿边坡稳定性分析及监测预测技术研究[硕士学位论文][D]. 厦门：厦门大学，2018.(CHEN Yanying. Study on stability and monitoring prediction method of open-pit slope[M. S. Thesis][D]. Xiamen：Xiamen University，2018.(in Chinese))
[3]	赵静波，李莉，高谦. 边坡变形预测的灰色理论研究与应用[J]. 岩石力学工程学报，2005，24(增2)：5 799–5 802.(ZHAO Jingbo，LI Li，GAO Qian. Research and application of the grey theory to slope deformation prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(Supp.2)：5 799–5 802.(in Chinese))
[2]	蒋刚，林鲁生，刘祖德，等. 边坡变形的灰色预测模型[J]. 岩土力学，2000，21(3)：244–246.(JIANG Gang，LIN Lusheng，LIU Zhude，et al. Prediction grey model for slope displacement[J]. Rock and Soil Mechanics，2000，21(3)：244–246.(in Chinese))
[1]	李庶林. 论我国金属矿山地质灾害与防治对策[J]. 中国地质灾害与防治学报，2002，13(4)：44–48.(LI Shulin. Study on the geological hazard in metal mines and its prevention countermeasures[J]. The Chinese Journal of Geological Hazard and Control，2002，13(4)：44–48.(in Chinese))
[4]	吴益平，滕伟福，李亚伟. 灰色–神经网络模型在滑坡变形预测中的应用[J]. 岩石力学与工程学报，2007，26(3)：632–636.(WU Yiping，TENG Weifu，LI Yawei. Application of grey-neural network model to landslide deformation prediction[J]. Chinese Journal of Rock Mechanics and Engineering，2007，26(3)：632–636.(in Chinese))
[6]	何勇兵. 多变量灰色预测模型在大坝安全监测中的应用[硕士学位论文][D]. 杭州：浙江大学，2003.(HE Yongbin. Application of multiple grey prediction model for monitoring dam safety[M. S. Thesis][D]. Hangzhou：Zhejiang University，2003.(in Chinese))
[8]	WANG C H，HSU L C. Using genetic algorithms grey theory to forecast high technology industrial output[J]. Applied Mathematics and Computation，2008，195(1)：256–263.
[10]	刘思峰，党耀国，王正新，等. 灰色预测技术方法[M]. 北京：科学出版社，2014：1–5.(LIU Sifeng，DANG Yaoguo，WANG Zhengxin，et al. The method of grey prediction technique[M]. Beijing：Science Press，2014：1–5.(in Chinese))