|
|
|
| STUDY OF PLS-LCF MODEL FOR ROCK BURST PREDICTION BASED ON PARTICLE SWARM OPTIMIZATION |
| YAN Zhixin1，2，HE Xiang2，GONG Bin2 |
(1. Key Laboratory of Mechanics on Disaster and Environment in Western China，Ministry of Education，Lanzhou University， Lanzhou，Gansu 730000，China；2. School of Civil Engineering and Mechanics，Lanzhou University，
Lanzhou，Gansu 730000，China) |
|
|
|
|
Abstract The prediction of rock burst is a complex systemic problem. It is necessary to consider the various factors affecting the rock burst comprehensively. But when variables to be considered are very large，the multicollinearity among variables will affect the objectivity of the analysis. In order to eliminate the adverse effect and effectively predict rock burst，the affecting factors for rock burst are analyzed by means of partial least-squares regression(PLSR). A new synthesis variable with better interpretation to the dependent variable is extracted and it can preferably overcome the multicollinearity among variables. The nonlinear relationship between composition values and rock burst grades is established according to the logistic curve function(LCF). The particle swarm optimization(PSO) with the global optimization is used to optimize the parameters of LCF. So far，a PLS-LCF prediction model of rock burst based on PSO is built. The test results of the model show a very good precision. The evaluation results obtained by applying the developed model to practical engineering are well consistent with the practical situation，which indicates that the model is feasible and effective for rock burst prediction.
|
|
Received: 13 August 2012
|
|
|
|
| [1] 郭 雷，李夕兵，岩小明，等. 基于BP网络理论的岩爆预测方法[J]. 工业安全与环保，2005，31(10)：32–34.(GUO Lei，LI Xibing，YAN Xiaoming，et al. Rock burst prediction methods based on BP network theory[J]. Industrial Safety and Environmental Protection，2005，31(10)：32–34.(in Chinese))
[2] 白明洲，王连俊，许兆义. 岩爆危险性预测的神经网络模型及应用研究[J]. 中国安全科学学报，2002，12(4)：65–69.(BAI Mingzhou，WANG Lianjun，XU Zhaoyi. Study on aneutral network model and its application to predict the risk of rock blast[J]. China Safety Science Journal，2002，12(4)：65–69.(in Chinese))
[3] 冯夏庭，赵洪波. 岩爆预测的支持向量机[J]. 东北大学学报：自然科学版，2002，23(1)：57–59.(FENG Xiating，ZHAO Hongbo. Prediction of rockburst using support vector machine[J]. Journal of Northeastern University：Natural Science，2002，23(1)：57–59.(in Chinese))
[4] 谢学斌，潘长良. 岩爆灾害的灰类白化权函数聚类预测方法[J]. 湖南大学学报：自然科学版，2007，34(8)：16–20.(XIE Xuebin，PAN Changliang. Rockburst prediction method based on grey whitenization weight function cluster theory[J]. Journal of Hunan University：Natural Sciences，2007，34(8)：16–20.(in Chinese))
[5] WOLD S，ALBANO C，DUNN Ⅲ，et al. Pattern recognition: finding and using regularities in multivariate data[C]// Proceedings of IUFOST Conference[C]. London，U.K.：[s.n.]，1983，146–189.
[6] 徐 飞，徐卫亚. 岩爆预测的粒子群优化投影寻踪模型[J]. 岩土工程学报，2010，32(5)：718–723.(XU Fei，XU Weiya. Projection pursuit model based on particle swarm optimization for rock burst prediction[J]. Chinese Journal of Geotechnical Engineering，2010，32(5)：718–723.(in Chinese))
[7] EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]// Proceedings of Sixth International Symposium on Micro Machine and Human Science. Nagoya. Japan：[s.n.]，1995，66–73.
[8] 沈 艳，郭 兵，古天祥. 粒子群优化算法及其与遗传算法的比较[J]. 电子科技大学学报，2005，34(5)：696–699.(SHEN Yan，GUO Bing，GU Tianxiang. Particle swarm optimization algorithm and comparison with genetic algorithm[J]. Journal of University Electronic Science and Technology of China，2005，34(5)：696–699.(in Chinese))
[9] 王元汉，李卧东，李启光，等. 岩爆预测的模糊数学综合评判方法[J]. 岩石力学与工程学报，1998，17(5)：493–501.(WANG Yuanhan，LI Wodong，LEE P K K，et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering，1998，17(5)：493–501.(in Chinese))
[10] 丁向东，吴继敏，李 健，等. 岩爆分类的人工神经网络预测方法[J]. 河海大学学报：自然科学版，2003，31(4)：424–427.(DING Xiangdong，WU Jimin，LI Jian，et al. Artificial neural network for forecasting and classification of rockbursts[J]. Journal of Hohai University：Natural Science，2003，31(4)：424–427.(in Chinese))
[11] 杨承祥，罗周全. 有岩爆倾向深井矿山采矿巷道的失稳模式分析及其控制技术[J]. 矿冶工程，2007，27(2)：1–4.(YANG Chengxiang，LUO Zhouquan. Analyses and control of unstable mode of laneway with rock-burst possibility in deep mine[J]. Mining and Metallurgical Engineering，2007，27(2)：1–4.(in Chinese))
[12] 倪富来. 神经网络及其在岩爆灾害预测中的应用[J]. 重庆建筑，2011，10(7)：33–36.(NI Fulai. Neural network and its application to predict rock burst[J]. Chongqing Architecture，2011，10(7)：33–36.(in Chinese))
[13] 王惠文. 偏最小二乘回归方法及其应用[M]. 北京：国防工业出版社，1999：178–182.(WANG Huiwen. The method and application of partial least squares regression[M]. Beijing：National Defence Industry Press，1999：178–182.(in Chinese))
[14] 金菊良，魏一鸣，丁 晶. 水质综合评价的投影寻踪模型[J]. 环境科学学报，2001，21(4)：431–434.(JIN Juliang，WEI Yiming，DING Jing. Projection pursuit model for comprehensive of water quality[J]. Acta Scientiae Circumstantiae，2001，21(4)：431–434.(in Chinese))
[15] 杨晓华，杨志峰，沈珍瑶，等. 水资源可再生能力评价的遗传投影寻踪方法[J]. 水科学进展，2004，15(1)：73–76.(YANG Xiaohua，YANG Zhifeng，SHEN Zhenyao，et al. Genetic projection pursuit method for evaluating water resources reproducible ability[J]. Advances in Water Science，2001，15(1)：73–76.(in Chinese))
[16] 宫凤强，李夕兵. 岩爆发生和烈度分级预测的距离判别方法及应用[J]. 岩石力学与工程学报，2007，26(5)：1 012–1 018.(GONG Fengqiang，LI Xibing. A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application[J]. Chinese Journal of Rock Mechanics and Engineering，2007，26(5)：1 012–1 018.(in Chinese))
[17] 刘章军，袁秋平，李建林. 模糊概率模型在岩爆烈度分级预测中的应用[J]. 岩石力学与工程学报，2008，27(增1)：3 095–3 103.(LIU Zhangjun，YUAN Qiuping，LI Jianlin. Application of fuzzy probability model to prediction of classification of rockburst intensity[J]. Chinese Journal of Rock Mechanics and Engineering，2008，27(Supp.1)： 3 095–3 103.(in Chinese))
[18] 杨金林，李夕兵，周子龙，等. 基于粗糙集理论的岩爆预测模糊综合评价[J]. 金属矿山，2010，(6)：26–29.(YANG Jinlin，LI Xibing，ZHOU Zilong，et al. A fuzzy assessment method of rock-burst prediction based on rough set theory[J]. Metal Mine，2010，(6)：26–29.(in Chinese)) |
|
|
|
2013, Vol. 32 
