岩石物理力学参数与纵波速度相关性的比较研究

IF 1.1 Q3 MINING & MINERAL PROCESSING
H. Fattahi, Mahdi Hasanipanah, N. Z. Ilghani
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引用次数: 7

摘要

在采矿和土木工程中,岩石和岩体的力学特性是制定方案的决定性因素。决定岩石在不同应力条件下如何反应的两个因素是纵波速度(PWV)及其各向同性。因此,实现一种高精度的PWV估计方法是一项非常重要的任务。这项工作研究了不同智能模型的使用,如多元自适应回归样条(MARS)、分类和回归树(CART)、数据处理组方法(GMDH)和基因表达编程(GEP)来预测PWV。然后使用几种误差统计量,即平方相关系数(R2)和均方根误差(RMSE)来评估所提出的模型。CART、MARS、GMDH、GEP模型得到的R2分别为0.983、0.999、0.995、0.998。CART、MARS、GMDH和GEP模型预测PWV的RMSE分别为0.037、0.007、0.023和0.020。根据上述数量,本文所提出的模型预测的PWV具有良好的性能。然而,所获得的结果表明,与GEP、GMDH和CART模型相比,MARS模型的预测效果更好。因此,可以为其他岩石力学和岩土工程领域的目标预测提供一个精确的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating correlation of physico-mechanical parameters and P-wave velocity of rocks: a comparative intelligent study
The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, achieving a high-accurate method to estimate PWV is a very important task. This work investigates the use of different intelligent models such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), group method of data handling (GMDH), and gene expression programming (GEP) for the prediction of PWV. The proposed models are then evaluated using several error statistics, i.e. squared correlation coefficient (R2) and root mean squared error (RMSE). The values of R2 obtained from the CART, MARS, GMDH, and GEP models are 0.983, 0.999, 0.995, and 0.998, respectively. Furthermore, the CART, MARS, GMDH, and GEP models predict PWV with the RMSE values of 0.037, 0.007, 0.023, and 0.020, respectively. According to the aforementioned amounts, the models presented in this work predict PWV with a good performance. Nevertheless, the results obtained reveal that the MARS model yields a better prediction in comparison to the GEP, GMDH, and CART models. Accordingly, MARS can be offered as an accurate model for predicting the aims in other rock mechanics and geotechnical fields.
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
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