Mechanical properties of bimrocks with high rock block proportion

Abstract

For the investigation of mechanical properties of the bimrocks with high rock block proportion, a series of laboratory experiments, including resonance frequency and uniaxial compressive tests, are conducted on the 64 fabricated bimrocks specimens. The results demonstrate that dynamic elastic modulus is strongly correlated with the uniaxial compressive strength, elastic modulus and block proportions of the bimrocks. In addition, the density of the bimrocks has a good correlation with the mechanical properties of cases with varying block proportions. Thus, three crucial indices (including matrix strength) are used as basic input parameters for the prediction of the mechanical properties of the bimrocks. Other than adopting the traditional simple regression and multi-regression analyses, a new prediction model based on the optimized general regression neural network (GRNN) algorithm is proposed. Note that, the performance of the multi-regression prediction model is better than that of the simple regression model, owing to the consideration of various influencing factors. However, the comparison between model predictions indicates that the optimized GRNN model performs better than the multi-regression model does. Model validation and verification based on fabricated data and experimental data from the literature are performed to verify the predictability and applicability of the proposed optimized GRNN model.

摘要

本文基于单轴压缩以及共振频率试验,对多组不同特征的高含石率胶结型土石混合体试件进行 测试,以探究其物理力学特性。试验结果显示,试件的动弹性模量与其单轴抗压强度、弹性模量以及 含石率均存在显著的相关性。此外,试件的密度以及基质强度也与其宏观力学性能密切相关。因此, 选取以上三项典型指标,对高含石率胶结型土石混合体的力学性能进行预测。除传统的回归分析手段 以外,本文通过遗传算法对广义回归神经网络算法进行优化,并建立了相应的预测模型。预测结果表 明,尽管多元回归分析相对于一元回归分析而言预测性能有所提高,但基于优化回归神经网络的预测 结果更为理想。分别采用试验数据以及文献中的数据,证实了所建立的预测模型具有良好的适应性和 理想的预测性能。