Geomechanical parameters identification by particle swarm optimization and support vector machine

DOI10.1016/J.APM.2009.01.011zbMath1205.86049OpenAlexW2013374428MaRDI QIDQ965561

Hong-Bo Zhao, Shunde Yin

Publication date: 24 April 2010

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2009.01.011

zbMATH Keywords

particle swarm optimizationsupport vector machineback analysisgeomechanical parameters identification

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Geostatistics (86A32)

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Inverse analysis for rock mechanics based on a high dimensional model representation ⋮ A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms ⋮ A procedure of parameter inversion for a nonlinear constitutive model of soils with shield tunneling ⋮ Learning errors of linear programming support vector regression ⋮ Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine ⋮ Integrating support vector regression with particle swarm optimization for numerical modeling for algal blooms of freshwater ⋮ Parameter identification of genetic regulatory network with time-varying delays via adaptive synchronization method

Cites Work

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• Optimal estimation ofin-situ ground stresses from displacement measurements
• Numerical identification of soil-structure interaction pressures
• Back analysis of model parameters in geotechnical engineering by means of soft computing
• A new displacement back analysis to identify mechanical geo‐material parameters based on hybrid intelligent methodology

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