预测岩石单轴抗压强度的机器学习方法:比较研究

IF 1.8 4区 地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Tao Wen, Decheng Li, Yankun Wang, Mingyi Hu, Ruixuan Tang
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引用次数: 0

摘要

岩石的单轴抗压强度(UCS)是评估岩石力学性质和构建工程岩体分类系统的关键指标。在实验室环境中测定单轴抗压强度最常用的方法既昂贵又耗时。因此,可以根据几种简单的实验室测试,包括点荷载强度、岩石密度、纵波速度、巴西抗拉强度、施密特硬度和邵氏硬度,采用间接测定法估算 UCS。本研究利用不同岩石类型的六组指数数据,采用三种非线性组合模型(即反向传播模型(BP)、粒子群优化模型(PSO)和最小二乘支持向量机模型(LSSVM))预测 UCS。此外,还根据四项性能预测指标对最佳预测模型进行了检验和筛选。结果显示,PSO-LSSVM 模型因其更高的性能容量而比其他两个模型更成功。六个数据集的预测 UCS 与测量 UCS 之比分别为 0.954、0.982、0.9911、0.9956、0.9995 和 0.993。当预测比率接近约 1 时,结果更为合理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning methods for predicting the uniaxial compressive strength of the rocks: a comparative study

The uniaxial compressive strength (UCS) of rocks is a critical index for evaluating the mechanical properties and construction of an engineering rock mass classification system. The most commonly used method for determining the UCS in laboratory settings is expensive and time-consuming. For this reason, UCS can be estimated using an indirect determination method based on several simple laboratory tests, including point-load strength, rock density, longitudinal wave velocity, Brazilian tensile strength, Schmidt hardness, and shore hardness. In this study, six data sets of indices for different rock types were utilized to predict the UCS using three nonlinear combination models, namely back propagation (BP), particle swarm optimization (PSO), and least squares support vector machine (LSSVM). Moreover, the best prediction model was examined and selected based on four performance prediction indices. The results reveal that the PSO–LSSVM model was more successful than the other two models due to its higher performance capacity. The ratios of the predicted UCS to the measured UCS for the six data sets were 0.954, 0.982, 0.9911, 0.9956, 0.9995, and 0.993, respectively. The results were more reasonable when the predicted ratio was close to a value of approximately 1.

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来源期刊
Frontiers of Earth Science
Frontiers of Earth Science GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.50
自引率
5.00%
发文量
627
期刊介绍: Frontiers of Earth Science publishes original, peer-reviewed, theoretical and experimental frontier research papers as well as significant review articles of more general interest to earth scientists. The journal features articles dealing with observations, patterns, processes, and modeling of both innerspheres (including deep crust, mantle, and core) and outerspheres (including atmosphere, hydrosphere, and biosphere) of the earth. Its aim is to promote communication and share knowledge among the international earth science communities
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