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 journalArticlepeer-review

74 Scopus citations


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
Issue number1
StatePublished - 1 Jan 2002


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


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