ANN and ANFIS performance prediction models for hydraulic impact hammers

This publication has 30 references indexed in Scilit:

• Determination of instantaneous breaking rate by Geological Strength Index, Block Punch Index and power of impact hammer for various rock mass conditions
  Tunnelling and Underground Space Technology, 2011
• Performance prediction of ımpact hammers by block punch ındex for weak rock masses
  International Journal of Rock Mechanics and Mining Sciences, 2009
• A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method
  Journal of Materials Processing Technology, 2008
• Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system
  Environmental Geology, 2007
• Utilisation of neural networks to identify the status of the cutting tool point
  Tunnelling and Underground Space Technology, 2006
• A neuro-fuzzy model for modulus of deformation of jointed rock masses
  Computers and Geotechnics, 2004
• A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock
  Engineering Applications of Artificial Intelligence, 2004
• A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition
  Engineering Geology, 2002
• The performance prediction of impact hammers from Schmidt hammer rebound values in Istanbul metro tunnel drivages
  Tunnelling and Underground Space Technology, 2002
• Fuzzy model for the prediction of unconfined compressive strength of rock samples
  International Journal of Rock Mechanics and Mining Sciences, 1999