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Keywords: machine learning
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Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-442
... simulates its physiology. neural network machine learning characteristic upstream oil & gas artificial intelligence fuzzy controller discontinuity shear strength stability dilation takagi-sugeno fuzzy controller ballivy application artificial neural network fuzzy logic danta neto...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-366
... characterization halite artificial intelligence sequence upstream oil & gas reservoir geomechanics lithology drilling operation machine learning application sediment inclination morphology anhydrite knowledge santos basin algorithm figure 6 solubility frequency incidence chaos indicator...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-369
... machine learning hydraulic fracture geometry hydraulic fracturing fracture geometry upstream oil & gas simulation dataset artificial intelligence tensile strength architecture experiment agreement forecasting hydraulic fracture geometry regression algorithm ann neuron operation...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-385
..., and hydrogeological, and engineering geological conditions of the URL site, to establish a 3D geological model, and finally to provide necessary data for URL design. This paper briefly introduces the main findings obtained from the URL site characterization. artificial intelligence machine learning borehole...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-454
... slope, whose stability condition is unknown. This method is a user-friendly and relatively easy to use in engineering practice. discontinuity dataset node persistence decision tree learning uniaxial compressive strength cart algorithm rock mechanic machine learning stability orientation...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-315
..., it consumes much time to draw sketches in front of the tunnel face, which is also a great risk to engineers' safety. neural network classification information artificial intelligence mask r-cnn reservoir characterization tunnel natm tunnel deep learning machine learning construction leakage...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-328
... with the increase in t/a ratio, approaching the shear strength of infill material itself when t/a = 2, defining it as a critical ratio. Also, this study showed that an increase in the joint asperities caused an increase in shear strength. 2.2 Artificial Neural Networks ANN is an important tool of machine learning...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-291
...-making frameworks. Probabilistic and supervised machine learning are two examples of methods that may be used to streamline data review for an expert user. This paper uses the backdrop of a tunnel case study to compare a Bayesian Belief Network (BBN) to predict the ground class as a function of tunnel...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-292
.... human computer interaction machine learning rnn algorithm artificial intelligence investigation rqd procedure equation derivative neural network information exploration operation variation estado unido mexicanos sensation figure 1 sequence geotechnical safety sediment spatial...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-293
... Abstract The past decade has shown a rapid increase in the successful application of Machine Learning techniques for a variety of challenging tasks. Potential for this is also seen in the automatic rockmass behavior classification of tunnel boring machine (TBM) advance-data. This study...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-173
...) and Arzuman (2009), applying neural network (NN) in the estimation of properties for characterization of rocks, lithological classification and other related themes. upstream oil & gas artificial intelligence validation neural network machine learning carnallite 1 halite 1 mechanofacies...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-198
... mechanical aspects. reservoir characterization indicator recognition health & medicine diagnostic medicine hazard artificial intelligence acoustic emission combination university salzburg rock mechanic sensor image analysis exemplary probe machine learning strength graz university...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-021
... characterization characteristic landslide susceptibility simulator scenario geothermal project management neural network manifestation neuro-construction vegetation electricity machine learning geothermal reservoir federal commission erosion hazard quantification Rock Mechanics for Natural...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-027
.... (1998) derived an analytic solution of surface settlement in the shield tunneling construction and verified its applicability through five practical cases. settlement levee yangtze river levee shield construction machine learning numerical simulation shield tunnel neural network shield...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-032
... computation and the Metropolis algorithm. north america government history matching artificial intelligence implementation posterior stdev reservoir simulation machine learning strength mcmc algorithm likelihood statistics application nclass 30 algorithm metropolis algorithm deviation...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-036
... intelligence correlation discontinuity kaiser criterion application multivariate statistic machine learning factor 2 rockfall susceptibility colorado rockfall hazard rating system reservoir characterization database estimation factor 1 roughness cophenetic correlation Rock Mechanics...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-049
..., a combination of LiDAR, high-resolution photography, thermal, and hyperspectral imaging is used to provide a more comprehensive mapping of rock masses. Potential applications of machine learning in rock mass characterization using remote sensing techniques are also discussed. 1 Introduction...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-058
... classification ningbo zhougongzhai reservoir reserves evaluation machine learning equation improvement reserves classification fragmentation stability bq equation standard coefficient engineering database ministry formula bq classification characteristic Rock Mechanics for Natural Resources...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-063
... crystals, amphibolite, biotite and garnet. Occurs in the mine central portion, along SW, NW and SE, being the most common rock in the pit, covering 62.3% of the mapped area. machine learning fuzzy k-means algorithm discontinuity interpretation visualization artificial intelligence orientation...
Proceedings Papers

Paper presented at the 14th ISRM Congress, September 13–18, 2019
Paper Number: ISRM-14CONGRESS-2019-067
... methods, numerical modelling, statistical analysis or machine learning techniques (García-Gonzalo et al., 2016; Goh et al., 2017). While numerical modelling is a convenient tool to simulate the mechanical response of the stopes after excavation, one of its drawbacks is the difficulty in obtaining reliable...

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