Neural Network Forecast Model in Deep Excavation

Author:

Jan J. C.1,Hung Shih-Lin2,Chi S. Y.3,Chern J. C.4

Affiliation:

1. PhD, Dept. of Civil Engineering, National Chiao Tung Univ., 1001 Ta Hsueh Rd., Hsinchu Taiwan 300, R.O.C.

2. Associate Professor, Dept. of Civil Engineering, National Chiao Tung Univ., 1001 Ta Hsueh Rd., Hsinchu Taiwan 300, R.O.C. (corresponding author). E-mail: slhung@cc.nctu.edu.tw

3. Researcher, Geotechnical Researcher Center, Sinotech Engineering Consultants, Inc., Basement No. 7, Ln. 26, Yat-Sen Rd., Taipei Taiwan 105, R.O.C.

4. Manager, Geotechnical Researcher Center, Sinotech Engineering Consultants, Inc., Basement No. 7, Ln. 26, Yat-Sen Rd., Taipei Taiwan 105, R.O.C.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Reference34 articles.

1. A Concurrent Adaptive Conjugate Gradient Learning Algorithm On Mimd Shared-Memory Machines

2. An adaptive conjugate gradient learning algorithm for efficient training of neural networks

3. Adeli H. and Hung S. L. (1995). Machine learning—Neural networks genetic algorithms and fuzzy systems Wiley New York.

4. Chi S. Y. Chern J. C. and Wang C. C. (1999). “Information construction approach for deep excavation.” Proc. Int. Symposium Geotechnical Aspects of Underground Construction in Soft Ground Balkema Rotterdam The Netherlands 471–476.

5. Clay anisotropy and braced wall behavior.;Clough G. W.;J. Geotech. Eng.,1981

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