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Keywords: machine learning
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Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-024
.... 2013.Submitted to WMC2013 23rd World Mining Congress. Serafim J.L., & Pereira J.P. 1983.Consideration of the geome- chanics classification of Bieniawski. Proc. Int. Symp. on Engineering Geology and Underground Constructions: (pp. 1133 1144). 164 metals & mining machine learning Upstream Oil...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-029
... Artificial Intelligence rev scale property reusch interpretation Reservoir Characterization calibration rock mass property seismicity GSI discontinuum model machine learning resolution levkovitch length scale reservoir geomechanics plastic strain correlation deformation pre...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-051
... formula. Ladanyi got shearing strength formula that could take the influence of rocky bridge into account in 1970. strength theory cohesion slope rock mass Artificial Intelligence formula integrity knowledge management Engineering coefficient machine learning strength internal friction...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-031
... developed rapidly. They have achieved important progresses in parameter optimization, fuzzy simulation and problem-solving of acceptableness observation data, etc (Kennedy et al., 2010). Upstream Oil & Gas identification rock type machine learning swarm optimization hybrid algorithm...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-047
... calculation results is slightly larger. Artificial Intelligence machine learning Wellbore Design Reservoir Characterization Upstream Oil & Gas friction cohesion algorithm hoek-brown criterion fitting algorithm fitting algorithm normal stress application shear strength Rock mechanics...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-023
... issues for builders and require more understanding about rock performance during excavations. Meanwhile, uncertainty of mechanical rock parameters is the "bottleneck" in the evaluation and prediction of caverns' stability and mechanical behavior of [9,10] . machine learning neural network...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-006
... Characterization Artificial Intelligence aleatory uncertainty Upstream Oil & Gas machine learning Wellbore Design knowledge flowchart construction information memory system London tunnel Rock mechanics epistemic uncertainty application Case Example Hudson rock engineering international...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-011
... Discharge plane conductivity Upstream Oil & Gas fracture plane test result hydraulic fracturing fractural plane characteristic hydrogeological test machine learning transmissivity rock mass in-situ hydrogeological test injection section attitude hydraulic conductivity investigation DP...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-120
... Characterization displacement information machine learning orientation observational approach design stage excavation guideline requirement riedmüller ground behaviour investigation interpretation tunnel discontinuity geotechnical design construction 2013. Taylor & Francis Group...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-125
... mass can be seen in Table 1. Geomechanical properties of rock masses are shown in Table 2. machine learning Upstream Oil & Gas Artificial Intelligence underground structure natural language classification RMR classification system classification method support system JH method...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-117
... epicenter coordinates with wave velocity as unknown, which effectively avoids the influence of inaccurate velocity. The studies above is all belong to mine micro-seismic monitoring field, however, little research is known on source location during linear tunnel excavation process. machine learning...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-077
.... machine learning neural network metals & mining Engineering Artificial Intelligence neural network model severity Reservoir Characterization Upstream Oil & Gas selection northeastern university estimation intensity diversion tunnel neural network estimation rockburst information...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-127
.... machine learning coal bed methane Upstream Oil & Gas Reservoir Characterization coalbed methane Chinese Journal reservoir geomechanics coal seam gas coal side loosen zone roadway coal mass rock side rock mass Rock mechanics characteristic electromagnetic wave fracture Artificial...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-147
... new design concepts are systematically programmed for rock slopes, underground openings and underground mines. A more definite explanation was publish in 2000 (Feng, 2000) after extensive validation of the research scheme. Artificial Intelligence Feng design concept machine learning...
Proceedings Papers

Paper presented at the ISRM SINOROCK 2013, June 18–20, 2013
Paper Number: ISRM-SINOROCK-2013-158
... with a sleek surface, the collocation of blasting positions and dynamite volume. factor analysis parameter optimization coefficient tunnel 4 machine learning resistance factor-regression model historical data Artificial Intelligence deviation regression analysis follow equation variance...
Proceedings Papers

Paper presented at the ISRM International Symposium on Rock Mechanics - SINOROCK 2009, May 19–22, 2009
Paper Number: ISRM-SINOROCK-2009-044
... representative of the event power (the units are given in dB), but are commonly referred to as energy calculations in the literature due to their approximately linear relationship with energy. machine learning strength prediction characteristic Pattern Recognition Upstream Oil & Gas initiation...
Proceedings Papers

Paper presented at the ISRM International Symposium on Rock Mechanics - SINOROCK 2009, May 19–22, 2009
Paper Number: ISRM-SINOROCK-2009-019
... strength anisotropic effect rock strength Artificial Intelligence Rock mechanics anisotropy strength machine learning reservoir geomechanics Upstream Oil & Gas discontinuity dip-direction anisotropic rock variation peak strength discontinuity mechanical anisotropy Ramamurthy specimen...
Proceedings Papers

Paper presented at the ISRM International Symposium on Rock Mechanics - SINOROCK 2009, May 19–22, 2009
Paper Number: ISRM-SINOROCK-2009-109
... ABSTRACT It is very difficult to predict accurately non-linear deformation time series of surrounding rock using general methods expecially under complex geological condition. A new method based on Gaussian Process (GP) machine learning, which is a newly developing machine learning method...
Proceedings Papers

Paper presented at the ISRM International Symposium on Rock Mechanics - SINOROCK 2009, May 19–22, 2009
Paper Number: ISRM-SINOROCK-2009-164
... of convergence and support performance monitoring, were reported by Jinye (1993), Sirikaew (1993), Praphal (1993), Tran (1994), Sriwisead (1996), Nitaramon (1997), Gurung and Iwao (1998) and Phienwej (1999). knowledge management evaluation Artificial Intelligence access tunnel machine learning...
Proceedings Papers

Paper presented at the ISRM International Symposium on Rock Mechanics - SINOROCK 2009, May 19–22, 2009
Paper Number: ISRM-SINOROCK-2009-089
... machine learning Engineering training process Artificial Intelligence displacement prediction result BP neural network back analysis intelligent elasto-plastic displacement back analysis network training baozhen tunnel algorithm mechanical parameter ga-svr algorithm initial population...

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