首页|期刊导航|西安科技大学学报|基于孔隙率和粒径的煤矸石力学性能预测试验研究

基于孔隙率和粒径的煤矸石力学性能预测试验研究OA北大核心CSTPCD

Experimental study on prediction of mechanical properties of coal gangue based on porosity and particle sizes

中文摘要英文摘要

多孔介质岩石孔隙结构与力学性能之间具有显著的相关关系.以晋陕地区采掘煤矸石典型砂岩为研究对象,采用筛分试验、压汞法、X射线衍射、单轴压缩试验等方法,分析了不同孔隙率下岩石的强度和基本理化性质,通过SPSS统计学原理构建了孔隙强度预测模型,探究了孔隙率分布参数对孔隙砂岩力学性能的影响规律.结果表明:孔隙率显著影响试样的破坏状态,岩体孔隙率越小,颗粒间胶结更为充分,接触面积增大,排列紧密,孔隙率越低,抗压强度越高;检验线性相关性显著率最高为孔隙率-0.929,其次弹性模量达-0.762,表观密度达-0.587;孔隙率与岩石抗压强度呈显著负相关相关,预测精度R2 达 0.896,Spearman相关系数达-0.954.研究解释了孔隙结构参数对岩石力学性能的影响,为岩石强度快速评价及预测模型研究具有重要的参考依据.

The significant correlation between the pore structure and mechanical properties of porous media rocks is a subject of in-depth study.This research specifically focused on typical sandstone sam-ples obtained from coal mining waste in the Shanxi-Shaanxi area of China.By employing a range of methods including sieving tests,mercury intrusion porosimetry,X-ray diffraction analysis,and uniaxial compression tests,the study analyzed the strength and basic physicochemical properties of rocks under different porosity levels.Through the application of SPSS statistical principles,a pore strength predic-tion model was developed,exploring the impact of porosity distribution parameters on the mechanical properties of porous sandstones.The results show that the porosity significantly affects the failure state of the sample.The smaller the porosity of the rock mass,the more sufficient the cementation between the particles,the larger the contact area,the closer the arrangement,the lower the porosity,and the higher the compressive strength.The highest significance rate of linear correlation is found to be porosi-ty of-0.929,followed by elastic modulus of-0.762 and apparent density of-0.587.The porosity is significantly negatively correlated with the compressive strength of rock.The prediction accuracy R2 is 0.896,and the Spearman correlation coefficient is-0.954.The study explains the influence of pore structure parameters on the mechanical properties of rock,providing important reference for the rapid e-valuation and prediction model of rock strength.

朱磊;刘治成;刘成勇;赵萌烨;贾金兑;丁自伟

中煤能源研究院有限责任公司,陕西 西安 710054中煤能源研究院有限责任公司,陕西 西安 710054中煤能源研究院有限责任公司,陕西 西安 710054中煤能源研究院有限责任公司,陕西 西安 710054西安科技大学 能源学院,陕西 西安 710054西安科技大学 能源学院,陕西 西安 710054

矿山工程

煤矸石理化性质力学性能孔隙率强度预测方法

coal ganguephysicochemical propertiesmechanical propertiesporositystrength predic-tion method

《西安科技大学学报》 2024 (6)

1083-1094,12

国家自然科学基金项目(52074209)中煤能源集团重大专项(ZMYHT∗CK-W-GSZYHLY-03-23-040)

10.13800/j.cnki.xakjdxxb.2024.0607

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