基于钻进参数实时预测土体力学性质的Stacking集成模型

Stacking integrated model for real-time prediction of soil mechanical properties based on drilling parameters

摘要:

岩土体物理力学参数对工程勘察、设计、施工等作业不可或缺，但常规取样试验或原位检测均存在明显的精度误差。据此本文提出基于勘察钻探的实时钻进参数，建立基于机器学习的随钻土体物理力学参数模型。通过采集位于珠海市国家高新技术产业开发区内20 m勘探孔的真实数据，将EP-200G型钻机实时随钻采集的钻压、扭矩和三轴振动作为输入数据，将全孔土体粘聚力、内摩擦角、含水量与弹性模量试验数据作为输出。基于建模数据分析，证明使用单算法的3类机器学习模型（支持向量机、神经网络和决策树）的预测精度最高仅为0.78，而基于Stacking理念的集成模型可将预测精度提升至最高0.98。结合该模型，进行了随钻参数与土体参数间的敏感性分析，证实当不同土体参数发生变化时，不同随钻参数会发生明显变化，证明了随钻参数预测土体参数的可靠性与适用性。

Abstract:

The physical and mechanical parameters of rock and soil are indispensable for engineering investigation， design， construction， and other operations， but conventional laboratory testing or in-situ tests have obvious accuracy errors. Accordingly， a real-drilling machine learning model was proposed in this paper which is used to predict the soil physical and mechanical parameters from drilling parameters. By collecting the actual data from several holes with the depth of 20 meters located in the National High-tech Industrial Development Zone of Zhuhai， the drilling pressure， torque， and triaxial vibration collected by the EP-200G drilling rig in real-time were used as input data， and the test data of soil cohesion， internal friction angle， water content and elastic modulus were used as output. Based on the established model， it is proved that the prediction accuracy of the machine learning models using single algorithms （including support vector machine， artificial neural networks and decision tree） can only reach 0.78 at most， while the integrated model based on the stacking concept can increase the prediction accuracy to a maximum of 0.98. Combined with this model， a sensitivity analysis between the drilling parameters and soil parameters was carried out， which confirmed that the drilling parameters would change significantly with the change of soil parameters， proving the reliability and applicability of using drilling parameters to predict soil parameters.