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Structural Engineering and Mechanics Volume 16, Number 6, December 2003 , pages 643-654 DOI: https://doi.org/10.12989/sem.2003.16.6.643 |
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Real-time modeling prediction for excavation behavior |
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Li-Feng Ni, Ai-Qun Li, Fu-Yi Liu, Honore Yin and J.R. Wu
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| Abstract | ||
| Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model. | ||
| Key Words | ||
| neural network; excavation; real-time modeling prediction; construction. | ||
| Address | ||
| Li-Feng Ni and Ai-Qun Li College of Civil Engineering, Southeast University, Nanjing, Jiangsu Province, 210096, P.R. China Fu-Yi Liu First Institute of Shenzhen General Institute of Design and Research, Shenzhen, P.R. China Honore Yin ENPC-LAMI, 8 Avenue Blaise Pascal, Cite Descartes, Champs-sur-Marne-77455, Marne La Vallee Cedex, France J.R. Wu Department of Building and Construction, City University of Hong Kong, Kowloon, Hong Kong, P.R. China | ||