Zongqing Zhou, Songsong Bai, Kaiwei Chu, Jinglong Li, Jiwei Sun, Meixia Wang, Yi Sun, Minghao Li, Yuhan Liu
{"title":"用XGBoost方法标定DEM宏、微观参数","authors":"Zongqing Zhou, Songsong Bai, Kaiwei Chu, Jinglong Li, Jiwei Sun, Meixia Wang, Yi Sun, Minghao Li, Yuhan Liu","doi":"10.1007/s10035-022-01264-0","DOIUrl":null,"url":null,"abstract":"<p>To solve the complicated macro and micro parameter calibration problem in the discrete element method (DEM) simulation of rock mechanics, macro parameter prediction and micro parameter inversion model are established based on the XGBoost model. Firstly, a parameter database for the uniaxial compressive test in DEM has been established by literature research and numerical simulation. The critical parameters in the uniaxial compressive test have been chosen with data and theoretical analysis. The influence of the number of selected parameters on accuracy has also been discussed. Then, the prediction model and inversion model have been established which can complete the calibration quickly. The two models were tested by test samples, and the accuracy of the model can generally reach more than 90%. This research has great significance for improving the efficiency of discrete element modeling.</p>","PeriodicalId":582,"journal":{"name":"Granular Matter","volume":"24 4","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Calibration of DEM macro and micro parameters via XGBoost method\",\"authors\":\"Zongqing Zhou, Songsong Bai, Kaiwei Chu, Jinglong Li, Jiwei Sun, Meixia Wang, Yi Sun, Minghao Li, Yuhan Liu\",\"doi\":\"10.1007/s10035-022-01264-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To solve the complicated macro and micro parameter calibration problem in the discrete element method (DEM) simulation of rock mechanics, macro parameter prediction and micro parameter inversion model are established based on the XGBoost model. Firstly, a parameter database for the uniaxial compressive test in DEM has been established by literature research and numerical simulation. The critical parameters in the uniaxial compressive test have been chosen with data and theoretical analysis. The influence of the number of selected parameters on accuracy has also been discussed. Then, the prediction model and inversion model have been established which can complete the calibration quickly. The two models were tested by test samples, and the accuracy of the model can generally reach more than 90%. This research has great significance for improving the efficiency of discrete element modeling.</p>\",\"PeriodicalId\":582,\"journal\":{\"name\":\"Granular Matter\",\"volume\":\"24 4\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2022-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Granular Matter\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10035-022-01264-0\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Granular Matter","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10035-022-01264-0","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Calibration of DEM macro and micro parameters via XGBoost method
To solve the complicated macro and micro parameter calibration problem in the discrete element method (DEM) simulation of rock mechanics, macro parameter prediction and micro parameter inversion model are established based on the XGBoost model. Firstly, a parameter database for the uniaxial compressive test in DEM has been established by literature research and numerical simulation. The critical parameters in the uniaxial compressive test have been chosen with data and theoretical analysis. The influence of the number of selected parameters on accuracy has also been discussed. Then, the prediction model and inversion model have been established which can complete the calibration quickly. The two models were tested by test samples, and the accuracy of the model can generally reach more than 90%. This research has great significance for improving the efficiency of discrete element modeling.
期刊介绍:
Although many phenomena observed in granular materials are still not yet fully understood, important contributions have been made to further our understanding using modern tools from statistical mechanics, micro-mechanics, and computational science.
These modern tools apply to disordered systems, phase transitions, instabilities or intermittent behavior and the performance of discrete particle simulations.
>> Until now, however, many of these results were only to be found scattered throughout the literature. Physicists are often unaware of the theories and results published by engineers or other fields - and vice versa.
The journal Granular Matter thus serves as an interdisciplinary platform of communication among researchers of various disciplines who are involved in the basic research on granular media. It helps to establish a common language and gather articles under one single roof that up to now have been spread over many journals in a variety of fields. Notwithstanding, highly applied or technical work is beyond the scope of this journal.