NEURAL NETWORK MODEL OF BLASTING PARAMETERS OPTIMIZATION OF CRANE BEAM AT ROCKWALL

Being required to leave less damage and to produce a smooth outline face,the high quality of excavation of crane beam at rockwall is specified in the excavation,and as a result,the excavation is a very difficulty one.Commonly the excavation parameters are determined by consulting the similar engineering and doing site blasting tests.But this method is subject to high operation cost and not ideal blasting effect.In this present paper,the optimum model for blasting parameters of anchoring rock beam is established by making use of a strong mapped function of the neural network technology with the typical samples of other practical smooth blasting parameters,like explosive type,crack of rock body and developing degree,hole diameter,hole depth,line charging density and minimum burden,as the main factors influencing the excavation effect. The experimental blasting parameters for crane rock beam are determined with the model,and are compared with the ones obtained from the experiments in-site with similar conditions.The results show that the in-site experimental blasting parameters are preferably identical to those of the optimum design.The examination of the blasting acoustic wave of the protective layer and platform of rock mass indicates that the blasting effect is satisfied and the loose ring of surrounding rock mass is the smallest.