..... Because the leavings of weathered soilcrete are able to be returned to the surround nature without pollutions in short period. cement formulation casing and cementing cement property cement chemistry Artificial Intelligence machine learning construction materials regression model...

... therefore attempts to develop methods of evaluating and quantifying the risk of failure of rock beds and slopes in road disaster management. Yen rainfall intensity slope failure contingency planning rock bed vehicle Artificial Intelligence traffic closure History machine learning average...

... parameters and then to estimate the safety factors by using slope design charts for circular failure surface or other calculation methods (Hoek and Bray, 1974). Reservoir Characterization Artificial Intelligence RMR classification system reservoir geomechanics Upstream Oil & Gas machine...

..., Optimization, and Machine Learning, Addison-Wesley Publishing Co., Inc., New York. Horikoshi, K. and Randolph, M.F.(1998), "A Contribution to Optimum Design of Piled Rafts", Geotechnique, 48(3), pp. 301_317. Schaffer, J. D., et. al.(1989), "A Study of Control Parameters Affecting Online Performance of Ge...

... space metals & mining orientation disintegration zone machine learning explosive breakage geophysical observation interpretation destruction stress field mining work Artificial Intelligence Reorganization peri-contour part synergetic process deposit 2000. CRC Press. Permission...

... the entire tunnelling operation. machine learning early stage total crown settlement face distance linear function displacement tunnel section total displacement construction tunnel tunnel diameter Artificial Intelligence prediction relative displacement unmeasured settlement crown...

... timely, on the other hand, some predicted data such as initial stress, and elastic modulus can be used for stability analysis. grey system tangent angle unstable time Artificial Intelligence type displacement-time curve displacement-time curve tunnel rockmass initial series machine...

... Artificial Intelligence displacement Excavation Procedure evaluation machine learning function parameter correlation excavation method rock mass ground transportation information excavation Case History procedure rail transportation accuracy prediction application sequential...

... Artificial Intelligence mudstone sandstone green schist Reservoir Characterization overburden tunnel support machine learning deformation rate correlation coefficient crown settlement overburden ratio Upstream Oil & Gas apparent coefficient lateral pressure rock type...

... of the world have been also gathered. These data are compiled and processed through an integrated data-base. Reservoir Characterization wellbore integrity natural language construction materials Upstream Oil & Gas machine learning text classification Wellbore Design assessment strength...

... machine learning material behavior NN soil model strain increment neural network boundary condition Stability Evaluation increment STABILITY EVALUATION FOR EXCAVATED SLOPE USING FI- NITE ELEMENT-NEURAL NETWORK HYBRID ALGORITHMS Hong-Ci Wu1, H.L.M.Martens2, Saad Al-jibouri2, Tai Bao1 and Xiao-Bin...

... primarily due to particle rolling, sliding and crushing. Artificial Intelligence modeling settlement nonlinear elasticity model application machine learning inverse problem resolution initial value landfill hydrostatic testing formulation solid waste management adjustment operation...

.... Reservoir Characterization exponent failure criterion Artificial Intelligence criterion prediction Upstream Oil & Gas test result criteria hoek-brown criterion MPa machine learning strength sigma 1 envelope failure envelope estimation equation triaxial strength rock mass sigma 3...

... the development and interpretation of design ground motions that contribute to poor practice. Reservoir Characterization density function hazard machine learning Upstream Oil & Gas ground motion operational safety Earthquake seismic hazard evaluation probability attenuation relation return...

... of geomaterials. For example, simple numerical procedures have been used for many years in geotechnical practice in the assessment of strength, analysis of soil consolidation, and estimation of slope stability. neural network civil engineering machine learning Upstream Oil & Gas mean effective...

.... educational setting ESP student Artificial Intelligence mistake-induced dialogue user freedom machine learning learning strategy knowledge average result dialogue application instruction educational content effective stress total vertical stress sequential test section paper-based approach...

... teaching of Geomechanics, Proceedings of the 1st Int. Conf. on Geotechnical Engineering Education and Training, Sinaia, Romania, I. Monoliu, I Antonescu and N. Radulescu (Eds A.A. Balkema, pp. 343-348. McCarthy, B. (1996) About Learning. Barrington , IL: Excel, Inc. machine learning Engineering...

... especially on slopes. Artificial Intelligence waste management environmental law civil engineer Proc machine learning geoenvironmental engineering course geotechnical engineering curriculum knowledge assessment student Engineer environmental geotechnology water management Engineering...

... and delivery modes. machine learning Reservoir Characterization Upstream Oil & Gas supplier investigation email structural geology Artificial Intelligence Simulation courseware effective stress availability University US government geotechnical engineering permeability Engineering...

... network Gorge Project artificial neural network rock engineering deformation machine learning excavation amount prediction excavation construction Engineering slope deformation study rock slope engineering weiya xu SLOPE DEFORMATION STUDY WITH ANN METHOD IN TGP (THREE GORGES PROJECT) Weiya...