热门
Knowledge-based and data-driven fuzzy modeling for rockburst prediction
文献类型：期刊论文
| 作者 | Gokceoglu, Candan1; Adoko, Amoussou Coffi2; Zuo, Qing Jun3; Wu, Li3 |
| 刊名 | INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
 |
| 出版日期 | 2013 |
| 卷号 | 61页码:86-95 |
| 关键词 | Rockburst Mamdani fuzzy inference system Takagi-Sugeno fuzzy inference system ANFIS Prediction modeling in rock engineering |
| ISSN号 | 1365-1609 |
| DOI | 10.1016/j.ijrmms.2013.02.010 |
| 英文摘要 | Since rockburst is a violent expulsion of rock in high geostress condition, this causes considerable damages to underground structures, equipments and most importantly presents serious menaces to workers' safety. Rockburst has been associated with thousands of accidents and casualties recently in China. Due to this importance, this research was intended to predict rockburst intensity based on fuzzy inference system (FIS) and adaptive neuro-fuzzy inference systems (ANFIS), and field measurements data. A total of 174 rockburst events were compiled from various published research works. Five different models were investigated. The maximum tangential stress, the uniaxial compressive strength, the uniaxial tensile strength of the surrounding rock and the elastic strain energy index were considered as the inputs while the actual rockburst intensity was the output. In some models, the inputs were extended to the stress coefficient and the rock brittleness coefficient. The results obtained from the study conclude that the knowledge-based F1S model shows lowest performance with 45.8%, 13.2%, 16.5% and 66.52% of the variance account for (VAF), root-mean square error (RMSE), mean absolute percentage error (MAPE) and the percentage of the successful prediction (PSP) indices, while the ANFIS model indicates the best performance with 92%, 1.71%, 0.94% and 95.6% of VAR, RMSE, MAPE and PSP indices, respectively. These results suggest that the developed models in the present study can be used for the rockburst prediction, and this may help to reduce the casualties sourced from the rockbursts. (C) 2013 Elsevier Ltd. All rights reserved. |
| WOS研究方向 | Engineering ; Mining & Mineral Processing |
| 语种 | 英语 |
| WOS记录号 | WOS:000320494600007 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/3468]  |
| 专题 | 岩土力学所知识全产出_期刊论文 国家重点实验室知识产出_期刊论文 |
| 作者单位 | 1.Hacettepe Univ, Dept Geol Engn; 2.Chinese Acad Sci, State Key Lab Geomech & Geotech Engn, Inst Rock & Soil Mech ; 3.China Univ Geosci, Fac Engn |
| 推荐引用方式 GB/T 7714 | Gokceoglu, Candan,Adoko, Amoussou Coffi,Zuo, Qing Jun,et al. Knowledge-based and data-driven fuzzy modeling for rockburst prediction[J]. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES,2013,61:86-95. |
| APA | Gokceoglu, Candan,Adoko, Amoussou Coffi,Zuo, Qing Jun,&Wu, Li.(2013).Knowledge-based and data-driven fuzzy modeling for rockburst prediction.INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES,61,86-95. |
| MLA | Gokceoglu, Candan,et al."Knowledge-based and data-driven fuzzy modeling for rockburst prediction".INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 61(2013):86-95. |
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。