Concrete Compressive Strength Prediction Using Neural Networks Based on Non-destructive Tests and a Self-calibrated Response Surface Methodology

This publication has 20 references indexed in Scilit:

• The Effect of FEM Mesh Density on the Failure Probability Analysis of Structures
  KSCE Journal of Civil Engineering, 2017
• Porosity of Self-compacting Concrete
  Procedia Engineering, 2015
• Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods
  Construction and Building Materials, 2012
• Practical evaluation of relationship between concrete resistivity, water penetration, rapid chloride penetration and compressive strength
  Construction and Building Materials, 2011
• NDT measurements for the prediction of 28-day compressive strength
  NDT & E International, 2010
• Response surface methodology (RSM) as a tool for optimization in analytical chemistry
  Talanta, 2008
• Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks
  Automation in Construction, 2006
• Effect of coarse aggregate quality on the mechanical properties of high strength concrete
  Construction and Building Materials, 2003
• Concrete strength by combined nondestructive methods simply and reliably predicted
  Cement and Concrete Research, 2000
• Response Surface Methodology
  IIE Transactions, 1996