Calculation of Morphological Characteristic Parameters of Sand Particles Based on Deep Learning

For projects such as tailings ponds, slopes, and foundations, loose materials such as rock, slag, and sand, which are composed of particles, often have low cohesion and rely mainly on friction to maintain stability. The shear strength parameters, namely, the internal friction angle and cohesion, are the core parameters that describe the mechanical properties of materials and are directly related to the engineering stability of the above projects. The shear strength properties of loose media are related to the geometric morphological characteristics of particles. Particles with high irregularity will increase the bite and friction of the contact interface between particles, thereby affecting the overall peak shear strength of the material. This study takes sand as the research object. Based on the Mask R-CNN algorithm in deep learning, a sand particle image dataset consisting of single, contact, and sand surface particles is established. An image segmentation model that can identify particles on the surface of the sand layer and obtain the corresponding particle mask is trained; a Python 3.11.4 program is written to automatically calculate seven characteristic parameters of particle morphological characteristics parameters, including the Feret major diameter, the particle Feret minor diameter, the particle aspect ratio, the particle roundness, the comprehensive shape coefficient, the roughness, and the convexity through the particle mask. This method can obtain the overall morphological characteristics of sand particles in real time and is a particle processing method that is a prerequisite for the subsequent rapid prediction of the strength properties of granular materials.