Neural network forecast model in deep excavation

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

*此作品的通信作者

研究成果: Article同行評審

73 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)59-65
頁數7
期刊Journal of Computing in Civil Engineering
16
發行號1
DOIs
出版狀態Published - 1 1月 2002

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