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An empirical approach of overbreak resistance factor for tunnel blasting

机译:隧道爆破抗冲破坏系数的经验方法

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The assessment of overbreak is proposed by means of a novel empirical approach; the 'overbreak resistance factor' (ORF), to predict and manage the overbreak phenomenon in tunnel drill-and-blast operations. The proposed ORF is formulated by analysing the relationship between uncontrollable parameters of the overbreak phenomenon, i.e., geological parameters, and the corresponding overbreak measurements. Ninety data sets were collected from the Shin-Hakoishi Tunnel operation in Japan. Initially, an identical weight was applied to all geological parameters to generate ORF subfactors. The contribution of these subfactors to the measured overbreak was analysed through the use of five overbreak prediction artificial neuron network (ANN) models. A sensitivity analysis was conducted on the ANN models to reveal the contributions of input factors to measured overbreak. The discontinuities factors demonstrated the highest influence on overbreak with an overall sensitivity of 55.20%, whereas the strength factors, the weathering factors and the face condition factors showed less sensitivity, at 27.18%, 9.43%, and 8.18% respectively. The sensitivity analysis results were applied back to the initial unweighted data sets to generate a weighted record of subfactors. The ORF values showed a clear inverse proportional relation to the measured overbreak values, through linear regression analysis. Consequently, a five-step ORF prediction chart was developed, which can be directly applied to estimate overbreak in any drill-and-blast tunnel project.
机译:通过一种新颖的经验方法对暴发进行评估。 “抗冲撞因素”(ORF),以预测和管理隧道钻爆作业中的冲撞现象。通过分析暴发现象的不可控制参数(即地质参数)与相应的暴发测量值之间的关系来制定拟议的ORF。从日本的新函石隧道运营中收集了90个数据集。最初,将相同的权重应用于所有地质参数以生成ORF子因子。通过使用五个超量预测人工神经元网络(ANN)模型,分析了这些子因素对测得的超量的贡献。对ANN模型进行了敏感性分析,以揭示输入因素对测得的爆发的贡献。不连续性因素对爆发的影响最大,总体敏感性为55.20%,而强度因素,耐候性因素和面部状况因素的敏感性较低,分别为27.18%,9.43%和8.18%。将灵敏度分析结果应用于原始的未加权数据集,以生成子因子的加权记录。通过线性回归分析,ORF值与测得的爆发值呈明显的反比例关系。因此,开发了一个五步ORF预测图,可将其直接用于估算任何爆破隧道项目中的超车量。

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