基于数字图像技术的含双预制裂隙类岩石试样力学特性细观研究

Meso research on the mechanical properties of rock specimens withdouble pre-existing fissures based on digital image correlation

岩石破坏过程中裂纹孕育演化规律及分布模式是解决岩石破坏问题的关键所在,而预制裂隙作为一种典型的岩石缺陷,与岩石的宏观力学状态和结构破坏特性之间有着密切的联系。采用数字图像技术(DIC)及颗粒流程序(PFC),研究了单轴压缩状态下含双预制裂隙类岩石试样破坏全过程,得到了试样破坏过程中观测面全场应变和位移的演化过程以及裂纹发展、微破裂数等特性。研究表明:①当载荷到达一定阶段,预制裂隙两端首先出现明显的微破裂区,其形状近似椭圆,开始形成宏观Ⅰ型裂纹(翼形张拉裂纹);②随着载荷的增大,微破裂聚集,预制裂隙端部开始形成宏观Ⅱ型裂纹(剪切裂纹),并与Ⅰ型裂纹一起持续发育直至试样破坏;③预制裂隙左右两侧和上下两侧的X方向和Y方向位移量差别较大,X方向位移量在两条预制裂隙的右边为正值,左边为负值;Y方向位移量自固定端(下端)向加载端呈由大到小呈梯度分布。

The cracks initiation and evolution and distribution patterns in rock failure process are the key to solve the rock failure problems.As a typical rock defect,fissures are closely related to the macroscopic mechanical state and structural damage characteristics of rocks.Using digital image correlation(DIC)and particle flow code(PFC),we study the whole process of fracture of rock specimens with double pre-existing fissures under uniaxial compression.And we obtained the evolution process of the strain and displacement of the entire surface of the observation surface during the fracture of the specimen,as well as the development of sample crack and the number of micro-fracture.The main findings are:①When the load reaches a certain stage,the first micro-cracks appear at both ends of the pre-existing fissures,and its shape is similar to the ellipse.Macro-I-type cracks(wing-type tensile cracks)begin to form.②With the increase of the load,the micro-fracture gathers,the macroscopic type II cracks(shear cracks)begin to form at the ends of the pre-existing fissures,and continue to develop with type I cracks until the specimen is destroyed.③The X,Y direction displacements of the left and right sides of the pre-existing fissures and the upper and lower sides are very different.The displacement in the X direction is positive on the right side of the two pre-existing fissures and negative on the left side;the displacement in the Y direction from the fixed side(lower)to the load side is from the largest to the smallest gradient distribution.