Determining the importance of factors affecting a ground-foundation system using artificial neural network and systems methodologies

  • Authors:

  • P. Lu;M. Rosenbaum

  • Affiliations:

  • Geohazards Group, School of Property and Construction, The Nottingham Trent University, Nottingham, United Kingdom;Geohazards Group, School of Property and Construction, The Nottingham Trent University, Nottingham, United Kingdom

  • Venue:
  • ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

Consideration is given to determining the importance of factors affecting a ground-foundation-structure system. A novel strategy has been developed for analysing the relative importance of factors by employing both Artificial Neural Network (ANN) and Rock Engineering System (RES) methodologies. ANN is mainly employed in situations where significant quantities of data are available; RES is primarily used to obtain a pragmatic solution in cases where the data are sparse. Used together, these two methodologies are able to compensate for each other's weaknesses and so derive a more realistic description of likely ground conditions. The application is illustrated by reference to examples that deal with the impact on low-rise structures of geohazards caused by soil volume change.