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Modeling Uniaxial Compressive Strength of Some Rocks from Turkey Using Soft Computing Techniques
Measurement ( IF 5.6 ) Pub Date : 2020-11-30 , DOI: 10.1016/j.measurement.2020.108781
Enes Gül , Engin Ozdemir , Didem Eren Sarıcı

Uniaxial compressive strength (UCS) is substantially used mechanical parameters to observe and classification of rocks, but this test is subsersive, taking a long time and required well equipped laboratory conditions and properly prepared samples. Therefore it is important to estimate this parameter from other physico-mechanical rock parameters that are nondestructive, easy to prepare samples and required less time. Thus the use of some new machine learning methods has become more attractive and widespread. Machine learning methods which are among these methods and increase their importance and validty are Multilayer Perceptron Neural Network (MLPNN), M5 Model Tree (M5MT), Extreme Learning Machine (ELM) methods. In this study, Brazilian tensile strength, ultrasonic P-vawe velocity, shore hardness tests of different rock types (Basalt, limestone, dolostone) were performed. The results were used for estimating UCS using MLPNN, M5MT, ELM methods. The validation of models were checked root mean squared error (RMSE), mean absolute error (MAE), variance account for (VAF) and coefficient of determination (R2). The MLPNN approach out performed the other approaches. Weights and bias values for MLPNN and ELM approaches and the tree structure for the M5MT method are presented.



中文翻译:

使用软计算技术模拟土耳其一些岩石的单轴抗压强度

单轴抗压强度(UCS)实际上是用来观察岩石和对其进行分类的机械参数,但是该测试是沉入式的,需要很长时间,并且需要装备精良的实验室条件和正确准备的样品。因此,重要的是从无损,易于制备样品且所需时间更少的其他物理力学岩石参数中估算该参数。因此,一些新的机器学习方法的使用变得越来越有吸引力和广泛。在这些方法中并提高其重要性和有效性的机器学习方法是多层感知器神经网络(MLPNN),M5模型树(M5MT),极限学习机(ELM)方法。在这项研究中,巴西抗拉强度,超声波P速度,不同岩石类型(玄武岩,石灰石,白云石)。结果用于使用MLPNN,M5MT,ELM方法估计UCS。检验模型的有效性,检查均方根误差(RMSE),平均绝对误差(MAE),方差占比(VAF)和确定系数(R2)。MLPNN方法执行了其他方法。给出了MLPNN和ELM方法的权重和偏差值,以及M5MT方法的树结构。

更新日期:2020-12-01
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