School of Civil and Resource Engineering, University of Science and Technology Beijing
Chongqing Zhonghuan Construction Co. , Ltd.

为建立凿岩机钻凿参数与岩石强度间的关系,进行岩石强度识别研究,搭建了台车钻凿参数自动采集系统,选用C30、C40、C50共3种强度混凝土模拟同等强度岩石,动态采集不同钻凿参数,并基于支持向量机(SVM)算法构建4种SVM分类模型,对钻凿数据进行训练学习并运用优化算法修正核函数系数,根据分类准确率及评价指标完成模型优选。研究表明,采用多项式核与高斯核函数建立的SVM模型识别准确率达90%,可以有效识别类岩石材料强度。

In order to establish the relationship between drilling parameters of rock drill and rock strength, and carry out research on rock strength identification, an automatic collection system of drilling parameters by truck was built. In the test, three kinds of strength concrete, C30, C40, and C50, were selected to simulate the rock of the same strength, and different drilling parameters were dynamically collected. Four SVM classification models were constructed based on the Support Vector Machine (SVM) algorithm. The drilling data was trained and learned, and the kernel function coefficient was modified by optimization algorithm. The model was optimized according to classification accuracy and evaluation indicators. The results show that the accuracy of SVM model based on polynomial kernel and Gaussian kernel function can reach 90%, which can effectively identify the strength of rock-like materials.

王喆,仇安兵,龚敏,等. 基于实测钻凿参数的类岩石材料强度识别研究[J]. 矿业安全与环保,2023,50(4):55-62.

WANG Zhe,QIU Anbing,GONG Min,et al. Research on strength identification of rock-like materials based on measured drilling parameters[J]. Mining Safety & Environmental Protection,2023,50(4):55-62.