Yi Tang, Hang Lin, Su Li, Yifan Chen, Ke Ou, Linglin Xie
{"title":"岩桥形状识别和岩桥破坏机理的数值研究","authors":"Yi Tang, Hang Lin, Su Li, Yifan Chen, Ke Ou, Linglin Xie","doi":"10.1007/s40571-024-00732-z","DOIUrl":null,"url":null,"abstract":"<p>Rock bridges are important structures for maintaining rock mass stability, but their shapes are not well known. The researchers propose a method for determining the shape of rock bridges based on experiments, discrete element methods and machine learning, which is applicable to complex joints with arbitrary spatial distribution. Numerical models are constructed using the discrete element method, and parameter matching is performed based on experimental results. The particles were clustered using the k-means algorithm with the maximum principal stress (<i>σ</i><sub>1</sub>) as an indicator and the selection of initial values was optimized. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to delete the noise from the particles. Finally, the boundary lines of the particles were extracted by self-programming, and the shape of the rock bridges was determined. Twenty-four sets of simulations were used to analyze the effect of rock bridges on the specimens. The results show that the failure mode of the specimen changes from shear to tensile damage as the cohesive force of the rock bridges increases. The peak strength and peak strain of the specimens increased with the increase of cohesion in the rock bridge. Rock bridges are the fastest growing areas of stress in the specimen.</p>","PeriodicalId":524,"journal":{"name":"Computational Particle Mechanics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Numerical study of rock bridge shape identification and rock bridge damage mechanism\",\"authors\":\"Yi Tang, Hang Lin, Su Li, Yifan Chen, Ke Ou, Linglin Xie\",\"doi\":\"10.1007/s40571-024-00732-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Rock bridges are important structures for maintaining rock mass stability, but their shapes are not well known. The researchers propose a method for determining the shape of rock bridges based on experiments, discrete element methods and machine learning, which is applicable to complex joints with arbitrary spatial distribution. Numerical models are constructed using the discrete element method, and parameter matching is performed based on experimental results. The particles were clustered using the k-means algorithm with the maximum principal stress (<i>σ</i><sub>1</sub>) as an indicator and the selection of initial values was optimized. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to delete the noise from the particles. Finally, the boundary lines of the particles were extracted by self-programming, and the shape of the rock bridges was determined. Twenty-four sets of simulations were used to analyze the effect of rock bridges on the specimens. The results show that the failure mode of the specimen changes from shear to tensile damage as the cohesive force of the rock bridges increases. The peak strength and peak strain of the specimens increased with the increase of cohesion in the rock bridge. Rock bridges are the fastest growing areas of stress in the specimen.</p>\",\"PeriodicalId\":524,\"journal\":{\"name\":\"Computational Particle Mechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Particle Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40571-024-00732-z\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Particle Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40571-024-00732-z","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Numerical study of rock bridge shape identification and rock bridge damage mechanism
Rock bridges are important structures for maintaining rock mass stability, but their shapes are not well known. The researchers propose a method for determining the shape of rock bridges based on experiments, discrete element methods and machine learning, which is applicable to complex joints with arbitrary spatial distribution. Numerical models are constructed using the discrete element method, and parameter matching is performed based on experimental results. The particles were clustered using the k-means algorithm with the maximum principal stress (σ1) as an indicator and the selection of initial values was optimized. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to delete the noise from the particles. Finally, the boundary lines of the particles were extracted by self-programming, and the shape of the rock bridges was determined. Twenty-four sets of simulations were used to analyze the effect of rock bridges on the specimens. The results show that the failure mode of the specimen changes from shear to tensile damage as the cohesive force of the rock bridges increases. The peak strength and peak strain of the specimens increased with the increase of cohesion in the rock bridge. Rock bridges are the fastest growing areas of stress in the specimen.
期刊介绍:
GENERAL OBJECTIVES: Computational Particle Mechanics (CPM) is a quarterly journal with the goal of publishing full-length original articles addressing the modeling and simulation of systems involving particles and particle methods. The goal is to enhance communication among researchers in the applied sciences who use "particles'''' in one form or another in their research.
SPECIFIC OBJECTIVES: Particle-based materials and numerical methods have become wide-spread in the natural and applied sciences, engineering, biology. The term "particle methods/mechanics'''' has now come to imply several different things to researchers in the 21st century, including:
(a) Particles as a physical unit in granular media, particulate flows, plasmas, swarms, etc.,
(b) Particles representing material phases in continua at the meso-, micro-and nano-scale and
(c) Particles as a discretization unit in continua and discontinua in numerical methods such as
Discrete Element Methods (DEM), Particle Finite Element Methods (PFEM), Molecular Dynamics (MD), and Smoothed Particle Hydrodynamics (SPH), to name a few.