高温作用后砂岩物理力学性质阈值温度的确定——基于机器学习算法

通过采用机器学习算法，提出了高温作用后砂岩物理力学性质变化的阈值温度的确定方法。基于Python语言，利用K-Means、SVM算法，实现了对样本数据的分类和聚类，确定了阈值温度区间，验证了二分类法的合理性和准确性.结果表明，砂岩样本的阈值温度区间为400~600 ℃，阈值温度上下砂岩的物理力学性质存在显著差异，体现为阈值温度区间以下的岩样物理力学性质较为分散，而阈值温度区间以上的岩样物理力学性质较为集中.

Determination of the Threshold Temperature of Physical and Mechanical Properties of Sandstone Post High Temperature by Machine Learning Algorithm

In this paper, machine learning algorithm is adopted to determine the threshold temperature of physical and mechanical properties changes of sandstone post high temperature. Based on Python language, SVM and K-means algorithm are employed to classify and cluster sample data, determine the threshold temperature range, and verify the rationality and accuracy of dichotomies. The results show that 1) the threshold temperature range of the sandstone sample is 400 ℃ ~ 600 ℃; 2) there are significant differences on the physical and mechanical properties of the sandstone around the threshold temperature. The physical and mechanical properties of the rock samples are relatively scattered below the threshold temperature range, while the physical and mechanical properties of the rock samples are relatively concentrated above the threshold temperature range.