摘要
针对BP神经网络算法存在局部极小问题、收敛对初值敏感问题及收敛速度慢问题,首次采用了一种新的神经网络算法,即样条函数神经网络算法,在破碎带工程围岩稳定性影响因素分析的基础上,来研究在破碎带工程围岩超前锚杆加固方式的优选问题。研究表明,超前锚杆拱部的支护参数在间距为0.4～0.6m,排距为0.4～0.5m,外插角10°～20°,锚杆直径在20～22mm的效果最佳。由此来研究锚杆超前支护的参数设置及如何提高岩体的稳固性,为破碎带岩层施工安全和局部强化支护提供理论依据。
Due to such problems as local minima, slow convergence, and dependence on initialized values arising by the back propagation （BP） neural network algorithms, it is the first time to adopt this new algorithm called the spline weight function artificial neural network. Based on the analysis on effects of stability in fracture surrounding rocks, the neu- ral network model is adopted to select an optimal parameters in forepoling bolt for the fractures. The study shows that the supporting parameters with 0.4 - 0.6 m in space, 0.4 -0.5 m in rank, 10° - 20° at angle and 20 - 22 mm in diameter are optimal for forepoling bolt. Based on this, how to select the parameter for forepoling bolt and how to improve the stability of rock body can be investigated, then providing a theoretical basis for safety construction and supporting strengthening in local place for the fracture rocks.
出处
《金属矿山》
CAS
北大核心
2009年第11期18-20,24,共4页
Metal Mine
基金
国家安全生产科技发展计划项目(编号:05-376)
"十一五"国家科技支撑计划项目(编号:2008BAB32B01)
国家高科技研究发展计划(863计划)项目(编号:2008AA062101)
关键词
样条权函数神经网络
超前锚杆加固
工程围岩
支护参数
Spline weight Function Artificial Neural Network, Forepoling Bolt, Surrounding rock in engineering, Supporting Parameters
