摘要
地下厂房岩锚梁开挖难度大、质量要求高,该部位成型质量的好坏将直接影响今后厂房内吊车的运行安全。通常凭借个人经验、工程类比或者在相似地段进行爆破试验,来决定爆破开挖参数。这样不仅费工费时,有时爆破效果也不理想。选择有代表性的实际光面爆破设计参数为样本,确定炸药类型、岩体裂隙发育程度、孔径、孔深、线装药密度、最小抵抗线、间距为主要因素,利用神经网络强大的非线性映射能力,建立岩锚梁爆破参数优化模型,进行优化设计。在相似地段进行保护层和岩台修面爆破试验,得出现场试验爆破参数,结果表明优化设计值与现场试验值吻合较好。声波检测表明,爆破质量良好,爆破松动圈范围最小。
Technology of excavating anchoring rock beam is the most difficult and demanded in excavating underground factory, its quality will affect its safety of performing later. Engineer makes decision depended on his experience, project contrast and test in similar condition before deciding its parameter. Much time and expenditure are wasted by the way. Moreover, the result is not as expect as what we have. The optimum model for blasting parameters of anchoring rock beam is established and its blasting parameters are optimized by making use of a strong mapped function of the neural network technology with the typical samples of other practical smooth blasting parameters. The explosive types, the extent of joint development of rock mass, the diameter of bore-hole, the depth of bore-hole, the linear charge density, the burden line of least resistance and the spacing of bore-hole are considered as the primary factors. The experimental blasting parameters for anchoring rock beam are determined by the in-site blasting experiments of the protective layer and platform of rock mass in similar conditions. The results show that the in-site experimental blasting parameters are preferably identical to those of the optimum design. The acoustic wave examination indicates that the blasting effect is satisfied and its smaller loose ring of surrounding rock mass is obtained.
出处
《水科学与工程技术》
2006年第2期52-54,共3页
Water Sciences and Engineering Technology
关键词
地下厂房
岩锚粱
爆破参数
爆破试验
人工神经网络
tmdergrotmd workshop
anchoring rock beam
blasting parameters
blasting experiments
BP neural networks
