基于微粒优化支持向量机的爆破振动强度预报及应用研究  

PREDICTION AND APPLICATION OF BLASTING VIBRATION INTENSITY BASED ON SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION ALGORITHM

作　　者：陆凡东[1] 方向[1] 沈蔚[1] 郭涛[1] 

机构地区：[1]解放军理工大学工程兵工程学院,南京210007

出　　处：《工程爆破》2009年第3期22-24,39,共4页Engineering Blasting

摘　　要：微粒优化支持向量机对爆破振动强度的预报能力优于经验公式法。比较高程差、水平距离、段药量、总药量、台阶高度、孔排距、抛掷方向和底盘抵抗线等8个输入参量不同组合的预报结果发现:前三个参量的组合预报效果最佳,预报相对误差仅为3.21%。Support vector machine with particle swarm optimization algorithm is superiority over the empirical formula method on the prediction ability of blasting vibration intensity. Comparing the prediction results of different combinations of eight input elements such as height difference, horizontal distance, maximum charge, total charge, bench height, hole and row spacing, cast direction and batholithic resistance line, the combination of the former three elements was found to give best results, with the relative error being only at the level of 3.21%.

关 键 词：爆破振动 强度预报 经验公式 支持向量机 

分 类 号：TD235.1[矿业工程—矿井建设]