西南交通大学学报2025，Vol.60Issue(3)：589-598,10.DOI:10.3969/j.issn.0258-2724.20230328
基于数字钻进参数的岩石强度确定方法
Determination Method of Rock Strength Based on Digital Drilling Parameters
摘要
Abstract
Rock strength is a critical parameter for assessing rock stability and safety.Efficient and accurate prediction of rock strength can effectively guide tunnel excavation and support.Digital drilling parameters and mechanical property data of rock were collected from various devices.By analyzing energy transfer during the drilling process,a quantitative relationship between digital drilling parameters and uniaxial compressive strength(UCS)was established.Meanwhile,machine learning methods were employed to develop a rock strength prediction model based on drilling parameters.Four algorithms,including a back-propagation(BP)neural network,random forest,convolutional neural network(CNN),and long short-term memory network were chosen to compare their prediction effects and identify the optimal model.The results indicate that compared to the theoretical formulas and the other three machine learning algorithms,the BP neural network algorithm excels in rock strength prediction,with a root mean square error of 5.794,a mean absolute error of 4.129,and a correlation coefficient of 0.9749.关键词
数字钻进参数/能量方法/抗压强度/神经网络/随机森林Key words
drilling parameters/energy method/compressive strength/neural networks/random forests分类
土木建筑引用本文复制引用
贾朝军,陈范雷,雷明锋,黄娟,施成华,刘帝..基于数字钻进参数的岩石强度确定方法[J].西南交通大学学报,2025,60(3):589-598,10.基金项目
国家自然科学基金项目(52378402) （52378402）
湖南省交通运输厅科技项目(202207) （202207）
