Estimation of groutability of permeation grouting with microfine cement grouts using RBFNN

  • Authors:

  • Kuo-Wei Liao;Chien-Lin Huang

  • Affiliations:

  • Department of Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan

  • Venue:
  • ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

The use of microfine cements in permeation grouting has been growing as a strategy in geotechnical engineering because it usually provides improved groutability (N). One of the major challenges of using microfine cement grouts is the ability to estimate the N within a reasonable level of error. The suitability of traditional groutability prediction formulas, which are mostly basis on the grain-size of the soil and the grout, is questionable for semi-nanometer scale grout. This study first investigated the accuracy of the current formulas; we found that the accuracy ranges from 45% to 68%, a level that is not adequate for practical engineering. An alternative approach, basis on a Radial Basis Function Neural Network (RBFNN), was developed. After finding a good correlation between the field observation and the RBFNN output, it was concluded that RBFNN is a suitable and reliable tool to predict the outcome of permeation grouting when microfine cement grout is used.