摘要
为研究含瓦斯水合物煤体强度预测模型的适用性,借鉴常规三轴强度准则计算格式和经验参数选取方法,通过理论推导和数据拟合获取强度预测模型,引入饱和度相关力学参数,建立考虑饱和度的含瓦斯水合物煤体强度预测模型。结果表明:指数、Rocker和Drucker-Prager强度预测模型的峰值强度预测值回归系数为0.897、0.985、0.994,平均相对误差为6.48%、2.48%、1.92%,说明Drucker-Prager、Rocker强度预测模型较为适用于含瓦斯水合物煤体;考虑饱和度的Drucker-Prager强度预测模型峰值强度预测值平均相对误差为1.69%,说明考虑饱和度的Drucker-Prager强度预测模型能够更加准确地预测含瓦斯水合物煤体的强度特性。
This paper is aimed at studying the applicability of the strength prediction model of gas hydrate-bearing coal.The targeted study consists of obtaining a strength prediction model through theoretical deduction and data fitting by means of the conventional calculation format of triaxial strength criterion and the selection of empirical parameters;developing the strength prediction model of the gas hydrate-bearing coal considering saturation by introducing related mechanical parameters of the saturation.The results show that the index,Rocker and Drucker-Prager strength prediction models appear the regression coefficients of the peak strength predictive value by 0.897,0.985 and 0.994,and the average relative errors up to 6.48%,2.48%and 1.92%,suggesting that Drucker-Prager and Rocker strength prediction models are more suitable for gas hydrate-coal mixture;and the average relative error of the peak strength predictive value of Drucker-Prager strength prediction model considering saturation is 1.69%,indicating that the Drucker-Prager strength prediction model considering saturation can predict the strength characteristics behind gas hydrate-coal mixture more accurately.
作者
高霞
杨书朋
刘飞
张保勇
吴强
Gao Xia;Yang Shupeng;Liu Fei;Zhang Baoyong;Wu Qiang(School of Architecture&Civil Engineering,Heilongjiang University of Science&Technology,Harbin 150022,China;School of Safety Engineering,Heilongjiang University of Science&Technology,Harbin 150022,China)
出处
《黑龙江科技大学学报》
CAS
2023年第6期817-823,共7页
Journal of Heilongjiang University of Science And Technology
基金
国家自然科学基金联合基金项目(U21A20111)
国家自然科学基金项目(51974112)。
关键词
含瓦斯水合物煤体
三轴压缩
饱和度
峰值强度
强度预测模型
gas hydrate-coal mixture
triaxial compression
saturation
peak strength
intensity prediction model