冯夏庭
博士研究生 教授
东北大学 东北大学资源与土木工程学院采矿系
智能岩石力学
暂无
- 姓名:冯夏庭
- 目前身份:在职研究人员
- 担任导师情况:
- 学位:
-
学术头衔:
博士生导师,
- 职称:高级-教授
-
学科领域:
岩土力学
- 研究兴趣:智能岩石力学
冯夏庭,1964年9月出生于安徽,教授,博士生导师,现任东北大学副校长、国家外专局教育部 “深部工程岩体力学与安全”学科创新引智基地负责人。曾任国际岩石力学学会主席、中国科学院武汉岩土力学研究所所长和岩土力学与工程国家重点实验室主任。国家自然科学基金委创新研究群体负责人。
主要从事深部工程岩体力学与安全研究工作,在岩体力学参数确定、岩石工程设计方法和岩爆监测预警与控制技术等方面,做出了突出贡献。主持国家973、863、国家自然科学基金国际合作重大、重点和部级等重大科研项目20余项,发表SCI收录论文180余篇、EI收录论文200余篇,出版中英文专著5部,获发明专利授权70余项,获国家科技进步二等奖4项(其中3项排名第1),省部级特等奖3项、一等奖10项。荣获国际岩石力学学会会士、国际岩土力学计算机方法和进展学会杰出贡献奖、首届全国创新争先奖、中国青年科技奖、光华工程科技奖等。
兼任国际地质工程联合会主席、国际岩石力学与工程学会岩石工程设计方法委员会主席、中国岩石力学与工程学会理事长、《岩石力学与工程学报》主编。
-
主页访问
4280
-
关注数
0
-
成果阅读
834
-
成果数
13
【期刊论文】Neural Network Assessment of Rockburst Risks for Deep Gold Mines in South Africa
冯夏庭
,-0001,():
-1年11月30日
-
67浏览
-
0点赞
-
0收藏
-
0分享
-
170下载
-
0评论
-
引用
【期刊论文】Some Thoughts Concerning the Development of Intelligent Rock Mechanics
冯夏庭
,-0001,():
-1年11月30日
-
61浏览
-
0点赞
-
0收藏
-
0分享
-
196下载
-
0评论
-
引用
-
59浏览
-
0点赞
-
0收藏
-
0分享
-
113下载
-
0评论
-
引用
-
75浏览
-
0点赞
-
0收藏
-
0分享
-
196下载
-
0评论
-
引用
冯夏庭, , 赵洪波
,-0001,():
-1年11月30日
针对岩爆预测的问题,提出了基于支持向量机的预测方法,通过对影响岩爆因素的分析,然后运用支持向量机理论建立岩爆预测的支持向量机模型,结果表明,基于支持向量机的岩爆预测方法具有较高的准确率,该方法是科学可行的,具有广泛的应前景。
统计学心理论,, 支持向量机,, 隧道,, 采场,, 岩爆,, 预测
-
71浏览
-
0点赞
-
0收藏
-
0分享
-
215下载
-
0评论
-
引用
冯夏庭, 马平波
,-0001,():
-1年11月30日
地下硐室围岩的稳定性评介是个极其复杂、知识匮乏的问题。首交将数据挖掘方法应用到地下硐室围岩稳定性判别知识的自学心中。鉴于现有数据挖掘方法未能考虑负属性的挖掘,提出了可能考虑负属怀的一种新的数据挖掘方法,它从硐室岩稳定性的实例数据中挖掘出知识、并将得到的知识输入专家系统,进行不确定的推理,对下硐室围岩的稳定性进行合理的判别。
数据挖掘,, 知识发现,, 专家系统,, 地下硐室,, 稳定性,, 知识自学心
-
96浏览
-
0点赞
-
0收藏
-
0分享
-
174下载
-
0评论
-
引用
【期刊论文】Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes1
冯夏庭, C.X.Yang, L.G. Tham, X.T.Feng, Y.J. Wang, P.K.K. Lee
,-0001,():
-1年11月30日
Based on genetic algorithm andgenetic programming, a new evolutionary algorithm isdeveloped to evolve mathematical models for prediction the behavior of complex systems. The input variablesof themodele are the property parameters of the systmes, which include thegeometry,the defromation, the strength parameters, such as displacement, stress, factor of safety, etc. To improve the efficiency of the evolution process, a two-stepped approach in adopted; the tow steps are the structure evolution and parameter optimization steps. In the structure evolution step, a family of model structures is generated by genetic programming. Eash model structure is a polynomial funcitonof the input variables. An interpreteristhen used to construct the mathematical expression for themodel through simplification, regularization, and rationalization. Furthermoere, necessary interanal model paramters are addedto themodel structures automatically. For each model structure, a genetic algortithm is then used to search for the best values of the internal model parameters in the parameter optimization step. The two steps are repeated tuntil the best model is evolved. The slope stability problem is used to demonstrate that the present method can efficiently generate mathematiocal models for predicting the behavior of complex engineering systems.
-
36浏览
-
0点赞
-
0收藏
-
0分享
-
105下载
-
0评论
-
引用
冯夏庭, Xiating Feng, Masahiro Seto
,-0001,():
-1年11月30日
roceed as C(t) ∞ tD, where the fractal dimentsion D is 0.43≤D<1.0. As the fractring process progresses, the system's state initially changes from ordered to disordered (fractal dimension D decreases form aobut 1 to abiout 0.48) and then changes back to ordered (fractal dimentsion increases form 0.48 to about 0.91). Corresponding to each evolutionary process of the system's states, AE event patterns such as the AE event rate, ae count rate, and amplitude in rock fracturing processes were recognized using neural network techniques. AE event patterns at 8-10 succedding time points were predicted using thecorresponding models. AE event patterns in rock microfractring processes are effectively described by the neural dynamic model NN(n,h,1). The models so obtained are applicable for extrapolatd recognition of AE event patterns with adequate accuracy. An improved learning algorthm is proposed to train the networks with generally improved performace of the models.
rock microfracturing process, acoustic emission, neural network, fractal, forecasting, multi-step extrapolating pattern recognition
-
130浏览
-
0点赞
-
0收藏
-
0分享
-
94下载
-
0评论
-
引用
【期刊论文】Fractal Structure of the Time Distribution of Microfracturing in Rocks
冯夏庭, Xia-Ting Feng, Masahiro Seto
,-0001,():
-1年11月30日
Using acousticemission data obtained from laboratory bouble torsion tests, we have analysed the fractal natureof aseries of 29 granited microfracturing processes in time. The data represent a wide variety of timescales, stress environments (increasing load with a constant displacement rate, relaxation, creep), soaking nditions [air, water, dodecyl trimethyl ammonium bormide (DTAB), poylethelene oxide (PEO)], and materialanisotroy. We fine that the timedistribution of rock microfraturing displays fractal and multifractal properties, In some cases, it hasasinglefractal of a multifractal structure. In other casese, it changes form a single fractal structure into a multifractal structure as the system eveoles dynamically. We suggest that the heterogeneity ofthe rock, the distribution of jojints of weak planes, the stress level, and the nature of the microfracturing mechanism lead to these multifractal properties. Whatever the fractal structure of the system, al lowerfractal dimentsion is generally produced at near-failure of the rockduee to an increased clusteing. This resultconcerning the fractal-dimension decrease is consistent with the conclusion drawn from the spatialdistribution of rock microfracturing. Thererore, from the vantage oint of observation of thime distribution of rock microfracturing, the decrease of the fractal dimension has a potential use as a rock failure predictor.
creep,, fractal,, fractal structure,, microfracturing,, relaxation,, time distribution.,
-
108浏览
-
0点赞
-
0收藏
-
0分享
-
117下载
-
0评论
-
引用
冯夏庭, Xiating Feng, Zhiqiang Zhang, Qian Sheng
,-0001,():
-1年11月30日
Establishing themechanical rock mass parameters isone of the important tasks for the highwall stability analysis of the permanent shiplock at the Three Gorges Project in China. Existing back analysismethodsare not sufficient to provide thenecessary arruracy and to recognize non-linear relations. The newdisplacement back analysis method proposed in this paper isa combination of a neural network, an evolutionary calcualtion, andnumerical analysis techniques. Tjhe non-linear relation involving displacementand mechanicalparameters is adequately recognized by theneural network techniques. the neural netwroks learn using an evolutionary technique, with samples created byorthogognal desing and tested with new cases given by event design. With the neural netwrik model established, the mechanical parameter are recognized using a genetic algorithm over a large search space in the golobal range. The predicted displacementccurring for each excavation step from January1998 to the end of excavation and their cumulative values for 5 later excavation steps are closely characterized by the new analysis technique.
-
39浏览
-
0点赞
-
0收藏
-
0分享
-
128下载
-
0评论
-
引用