Prediction of tunnel convergence using Artificial Neural Networks

Tunnelling and Underground Space Technology

Volumn 28, Issue 1, 2012, Pages 218-228

  Mahdevari, Satar ^a   Torabi, Seyed Rahman ^a,b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b SHAHROOD UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial Neural Network; Ghomroud tunnel; Monitoring; Non linear regression; Tunnel convergence

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CONVERGENCE MODEL; DATA SETS; EFFECTIVE FACTORS; FEED-FORWARD BACK PROPAGATION; GEOLOGICAL ENVIRONMENT; GEOLOGICAL PARAMETERS; LABORATORY TEST; MULTI LAYER PERCEPTRON; NON-LINEAR REGRESSION; NON-LINEAR RELATIONSHIPS; PARAMETRIC STUDY; RADIAL BASIS FUNCTIONS; ROCK MASS; ROCK MECHANICS PARAMETERS; SITE INVESTIGATIONS; STATISTICAL APPROACH; TRAINED NEURAL NETWORKS; TUNNEL BORING MACHINES; WATER CONVEYANCE;

BORING MACHINES (MACHINE TOOLS); CONSTRUCTION EQUIPMENT; FORECASTING; METAMORPHIC ROCKS; MONITORING; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; ROCK MECHANICS; SEDIMENTARY ROCKS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; GEOMECHANICS; LABORATORY METHOD; MONITORING; NUMERICAL MODEL; PREDICTION; REGRESSION ANALYSIS; ROCK MASS RESPONSE; SITE INVESTIGATION; TBM; TUNNEL DESIGN;

EID: 84856223727     PISSN: 08867798     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tust.2011.11.002     Document Type: Article

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