Modelling of particle flow code geotechnical material parameter relationships based on orthogonal design and back propagation neural network
文献类型：期刊论文
| 作者 | Ni, Yaodong1; Wang, Ruirui1; Leng, Xianlun2; Xia, Fengmin1; Wang, Feng1 |
| 刊名 | COMPUTATIONAL PARTICLE MECHANICS
 |
| 出版日期 | 2024-08-16 |
| 页码 | 28 |
| 关键词 | Discrete element method (DEM) Particle flow code (PFC) Orthogonal design Back propagation (BP) Neural network |
| ISSN号 | 2196-4378 |
| DOI | 10.1007/s40571-024-00806-y |
| 英文摘要 | The utilisation of particle flow code to establish discrete element models represents an effective approach for addressing the issue of discontinuous media. This methodology has been employed by numerous scholars to analyse the mechanical properties and damage laws of geotechnical materials. However, the complex nature of the particle action mechanism within the discrete element model necessitates a considerably longer time frame for the completion of an elaborate simulation experiment than that required for a laboratory test. This presents a significant challenge for researchers seeking to investigate the mechanical properties of a large number of geotechnical materials through the discrete element method. In order to accelerate the prediction of mechanical properties for various specific discrete element models, a mathematical model of the geotechnical micro-parameters and the geotechnical strength macro-parameters has been developed using an orthogonal design considering interactions and a back propagation neural network based on Bayesian regularisation. The geotechnical strength macro-parameters, such as compressive strength and tensile strength, can be derived directly from the geotechnical micro-parameters of the discrete element models through this mathematical model. The results show that the trained network model demonstrates an aptitude for predicting the uniaxial compressive strength, tensile strength, cohesion, and friction angle of geotechnical materials. The mean square error is 11.611 for the training set and 14.207 for the test set. In the test set, the median deviation rates of the predicted values of the four strength macro-parameters from the target values are 3.90%, 4.82%, 4.30%, and 7.30%. |
| 资助项目 | National Natural Science Foundation of China[52379110] ; Natural Science Foundation of Shandong Province[ZR202103010903] ; Doctoral Fund of Shandong Jianzhu University[X21101Z] |
| WOS研究方向 | Mathematics ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001291933400001 |
| 出版者 | SPRINGER INT PUBL AG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42254]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Wang, Ruirui |
| 作者单位 | 1.Shandong Jianzhu Univ, Sch Civil Engn, Jinan 250101, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ni, Yaodong,Wang, Ruirui,Leng, Xianlun,et al. Modelling of particle flow code geotechnical material parameter relationships based on orthogonal design and back propagation neural network[J]. COMPUTATIONAL PARTICLE MECHANICS,2024:28. |
| APA | Ni, Yaodong,Wang, Ruirui,Leng, Xianlun,Xia, Fengmin,&Wang, Feng.(2024).Modelling of particle flow code geotechnical material parameter relationships based on orthogonal design and back propagation neural network.COMPUTATIONAL PARTICLE MECHANICS,28. |
| MLA | Ni, Yaodong,et al."Modelling of particle flow code geotechnical material parameter relationships based on orthogonal design and back propagation neural network".COMPUTATIONAL PARTICLE MECHANICS (2024):28. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。