CALIBRATION METHOD OF MICROSCOPIC PARAMETERS FOR SIMILAR MATERIAL OF SURROUNDING ROCK BASED ON DEM
INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING(2024)
Abstract
Similar materials of surrounding rock are used to simulate the rock mass in the geomechanical model test. The discrete element method has the advantage of simulating the behavior of fractures between particles at the micro -scale, which can further reveal the failure mechanism of surrounding rock in combination with the model test. However, microparameters need to be calibrated before the simulation. In this paper, three kinds of bond models are described, and their application is analyzed. The soft -bond model is determined as the constitutive model of particles' contacts. Then, the simulation method of the biaxial test is introduced in detail, and the simulation results of the rigid -wall and flexiblewall methods are compared. Furthermore, based on the control variable method, a large number of biaxial tests are carried out by the rigid -wall method. Through single -factor sensitivity analysis and multi -factor variance analysis, the qualitative relationship between macroand micro -parameters and the significant influencing factors of each macroparameter are obtained. On this basis, the multivariate nonlinear multi -scale mathematical model is established by regression analysis. The appropriate micro -parameters are obtained by solving the proposed mathematical model using three optimization methods combined with the results of laboratory test measurements. This entire process constitutes the calibration method proposed in this paper. The reliability of the calibration method in this paper is verified by comparing the calculated macro -parameters, stress -strain curves, and failure modes with those of laboratory tests.
MoreTranslated text
Key words
discrete element method,biaxial compression test,similar materials,soft bond model,micro-parameter calibration,optimization
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2006
被引用215 | 浏览
2019
被引用4 | 浏览
2019
被引用28 | 浏览
2020
被引用35 | 浏览
2020
被引用27 | 浏览
2020
被引用24 | 浏览
2018
被引用26 | 浏览
2021
被引用11 | 浏览
2022
被引用75 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest