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Investigation of the effect of impact direction on Schmidt rebound values by multivariate regression and neuro‑fuzzy model

机译:多元回归与神经模糊模型对施米特反弹价值影响的影响

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Schmidt hammer test is a nondestructive test used in rock characterization and estimation of mechanical properties of rocks. It is an important advantage that the experiment can be carried out for different impact directions. However, a normalization should be performed on the test results because of variation of effect of gravitational forces on rebound value. On the other hand, some weak, highly porous and weathered rocks have different energy absorbance levels that may not confirm the assumption of normalization formula of the suggested method. So, in this study it is aimed to examine the prediction performance of suggested normalization formulation for weak and highly porous rocks. For this purpose, Schmidt hammer tests for five different impact directions were performed on eight different rock types. The test results indicated that, in general, the rebound values obtained for an impact direction other than horizontal can be converted to the rebound value of horizontal impact direction with a high accuracy by using suggested normalization formula and/or chart. However, existence of some exception was also determined for weak and highly porous rock types. Multivariate regression analyses and adaptive-neuro-fuzzy inference system (ANFIS) were used to explain the variation of ratio between the normalized and tested rebound values for rock types used in this study. The results obtained from statistical and soft computing methods showed that the ratio between the normalized and tested rebound values is predictable and higher for weak and highly porous rock types.
机译:施密特锤试验是用于岩石表征和岩石力学性能估计的非破坏性测试。这是一个重要的优点,即可以对不同的冲击方向进行实验。然而,由于引力力对回弹值的影响变化,应对测试结果进行归一化。另一方面,一些弱,高度多孔和风化的岩石具有不同的能量吸光度水平,可能无法确认建议方法的标准化公式的假设。因此,在本研究中,旨在检查弱和高孔岩石的建议标准化配方的预测性能。为此目的,在八种不同的岩石类型上进行了五种不同冲击方向的施密锤测试。测试结果表明,通常,通过使用建议的归一化公式和/或图表,可以通过高精度地将基于水平的碰撞方向的反弹值转换为水平冲击方向的回弹值。然而,还确定了一些例外的存在对于弱和高度多孔的岩石类型。多变量回归分析和自适应 - 神经模糊推理系统(ANFIS)用于解释本研究中使用的岩石类型的标准化和测试回弹值之间的比例的变化。从统计和软计算方法获得的结果表明,对于弱和高度多孔的岩石类型,标准化和测试的回弹值之间的比率可预测和更高。

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