Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.展开更多
针对地震和风振下结构的振动控制,提出了结构主被动复合调谐控制的策略,以及采用直线电机驱动主动质量阻尼器(active mass damper,简称AMD)、中空橡胶隔震支座作为调谐质量阻尼器(tune mass damper,简称TMD)弹性单元、滑轨作为TMD支撑...针对地震和风振下结构的振动控制,提出了结构主被动复合调谐控制的策略,以及采用直线电机驱动主动质量阻尼器(active mass damper,简称AMD)、中空橡胶隔震支座作为调谐质量阻尼器(tune mass damper,简称TMD)弹性单元、滑轨作为TMD支撑轨道的主被动复合调谐控制装置实现方法,进行了主被动复合调谐控制对结构动力响应控制的效果及减震机理分析,探讨了反馈响应向量对控制效果、AMD控制力和AMD位移的影响,完成了线性二次高斯算法(linear quadratic Gaussian,简称LQG)控制算法和H2/H∞控制算法时的主被动复合调谐控制结构振动台试验。研究结果表明:结构主被动复合调谐控制能有效减小结构的动力响应,改善TMD的性能;采用直线电机驱动的AMD作为主动控制装置,为防止AMD"飘移",AMD位移应作为目标向量和反馈响应;LQG控制算法总体控制效果优于H2/H∞控制算法。试验验证了提出的主被动复合调谐控制硬件系统方案的可行性,为工程应用提供了支撑。展开更多
掌握玻璃陶瓷MACOR的拉伸性能,对于其在航空航天、国防和其它工程中的应用,以及材料科学本身而言具有重要价值。巴西圆盘实验方法被用于研究加载速率对MACOR拉伸强度的影响。静态实验是在MTS试验机上完成的,动态实验是在6.35 mm SHPB实...掌握玻璃陶瓷MACOR的拉伸性能,对于其在航空航天、国防和其它工程中的应用,以及材料科学本身而言具有重要价值。巴西圆盘实验方法被用于研究加载速率对MACOR拉伸强度的影响。静态实验是在MTS试验机上完成的,动态实验是在6.35 mm SHPB实验装置中完成的。脉冲整形技术被用于实现试件两端力的动态平衡,以消除惯性效应的影响,从而实现准静态应力分析。实验结果表明,MACOR的拉伸强度与加载率相关,拉伸强度作为加载率的函数,当加载速率从0增加到5 780 GPa/s时,MACOR的拉伸强度从26 MPa增大到50 MPa。展开更多
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
基金Project (50934006) supported by the National Natural Science Foundation of ChinaProject (2010CB732004) supported by the National Basic Research Program of ChinaProject (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China
文摘The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.
文摘针对地震和风振下结构的振动控制,提出了结构主被动复合调谐控制的策略,以及采用直线电机驱动主动质量阻尼器(active mass damper,简称AMD)、中空橡胶隔震支座作为调谐质量阻尼器(tune mass damper,简称TMD)弹性单元、滑轨作为TMD支撑轨道的主被动复合调谐控制装置实现方法,进行了主被动复合调谐控制对结构动力响应控制的效果及减震机理分析,探讨了反馈响应向量对控制效果、AMD控制力和AMD位移的影响,完成了线性二次高斯算法(linear quadratic Gaussian,简称LQG)控制算法和H2/H∞控制算法时的主被动复合调谐控制结构振动台试验。研究结果表明:结构主被动复合调谐控制能有效减小结构的动力响应,改善TMD的性能;采用直线电机驱动的AMD作为主动控制装置,为防止AMD"飘移",AMD位移应作为目标向量和反馈响应;LQG控制算法总体控制效果优于H2/H∞控制算法。试验验证了提出的主被动复合调谐控制硬件系统方案的可行性,为工程应用提供了支撑。
