Neural network forecast model in deep excavation

J. C. Jan*, Shih-Lin Hung, S. Y. Chi, J. C. Chern

*Corresponding author for this work

Research output: Contribution to journalArticle

55 Scopus citations

Abstract

Diaphragm wall deflection is an important field measurement in deep excavation. The monitoring data are applied to evaluate the construction performance to avoid a supporting system failure or damages incurred to adjacent structures. Despite the numerous case histories of construction projects and several forecasting methods, no method accurately forecasts the performance of construction due to the complicated geotechnical and construction factors affecting the behavior of the diaphragm wall. This work predicts the diaphragm wall deflection by using the adaptive limited memory-Broyden-Fletcher-Goldfarb-Shanno supervised neural network. Eighteen case histories of deep excavations with four to seven excavation stages are selected for training and verification. In addition, the knowledge representation adopts measured wall deflections of previous excavation stages as inputs to the network. Doing so substantially reduces the importance of soil parameters, which are often extremely fluctuating and difficult to assess. Simulation results indicate that the artificial neural network can reasonably predict the magnitude, as well as the location, of maximum deflection of the diaphragm wall.

Original languageEnglish
Pages (from-to)59-65
Number of pages7
JournalJournal of Computing in Civil Engineering
Volume16
Issue number1
DOIs
StatePublished - 1 Jan 2002

Keywords

  • Algorithm
  • Diaphragm wall
  • Excavation
  • Geotechnical engineering
  • Neural networks
  • Sensitivity analysis

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