...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of African earth sciences >Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks
【24h】

Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks

机译：支持向量机和相关向量机在火山岩单轴抗压强度预测中的应用。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The uniaxial compressive strength (UCS) of intact rocks is an important and pertinent property for characterizing a rock mass. It is known that standard UCS tests are destructive, expensive and time-consuming task, which is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models have become an attractive alternative for engineering geologists. In the last several years, a new, alternative kernel-based technique, support vector machines (SVMs), has been popular in modeling studies. Despite superior SVM performance, this technique has certain significant, practical drawbacks. Hence, the relevance vector machines (RVMs) approach has been proposed to recast the main ideas underlying SVMs in a Bayesian context. The primary purpose of this study is to examine the applicability and capability of RVM and SVM models for predicting the UCS of volcanic rocks from NE Turkey and comparing its performance with ANN models. In these models, the porosity and P-durability index representing microstructural variables are the input parameters. The study results indicate that these methods can successfully predict the UCS for the volcanic rocks. The SVM and RVM performed better than the ANN model. When these kernel based models are considered, RVM model found successful in terms of statistical performance criterions (e.g., performance index, PI values for training and testing data are computed as 1.579 and 1.449). These values for SVM are 1.509 and 1.307. Although SVM and RVM models are powerful techniques, the RVM run time was considerably faster, and it yielded the highest accuracy.
机译：完整岩石的单轴抗压强度（UCS）是表征岩体的重要且重要的属性。众所周知，标准的UCS测试是破坏性，昂贵且费时的任务，对于薄层状，高裂缝性，叶状，高孔隙度和弱岩石尤其如此。因此，预测模型已成为工程地质学家的一种有吸引力的替代方法。在过去的几年中，一种新的，基于替代内核的技术，即支持向量机（SVM），已在建模研究中流行。尽管具有出色的SVM性能，但该技术仍存在某些明显的实际缺陷。因此，已经提出了相关性向量机（RVM）方法，以在贝叶斯上下文中重塑支持SVM的主要思想。这项研究的主要目的是检验RVM和SVM模型在预测土耳其东北部火山岩的UCS方面的适用性和功能，并将其性能与ANN模型进行比较。在这些模型中，代表微结构变量的孔隙率和P耐久性指标是输入参数。研究结果表明，这些方法可以成功地预测火山岩的UCS。 SVM和RVM的性能优于ANN模型。当考虑这些基于内核的模型时，根据统计性能标准（例如，性能指标，用于训练和测试数据的PI值计算为1.579和1.449），发现RVM模型是成功的。 SVM的这些值为1.509和1.307。尽管SVM和RVM模型是强大的技术，但RVM运行时间明显更快，并且产生了最高的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号