Prediction of Soil Composition from CPT Data Using General Regression Neural Network-Reference-Cited by-同舟云学术

Prediction of Soil Composition from CPT Data Using General Regression Neural Network

Published:2006-07 Issue:4 Volume:20 Page:281-289
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Kurup Pradeep U.¹²,Griffin Erin P.¹²

Affiliation:

1. Professor, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854.

2. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290887-3801%282006%2920%3A4%28281%29

Reference17 articles.

1. Douglas B. J. and Olsen R. S. (1981). “Soil classification using electric cone penetrometer.” Cone penetration testing and experience G. M. Norris and R. D. Holtz eds. ASCE New York 209–227.

2. SOIL LABORATORY DATA INTERPRETATION USING GENERALIZED REGRESSION NEURAL NETWORK

3. Itani O. M. and Najjar Y. M. (2000). “Three-dimensional modeling of spatial soil properties via artificial neural networks.” Transportation Research Record. 1709 Transportation Research Board Washington D.C. 50–59.

4. Juang C. H. (2002). “Soil liquefaction in the 1999 Chi-Chi Taiwan earthquake in situ test data.” ⟨ http://www.ces.clemson.edu/chichi/TW-LIQ/In-situ-Test.htm ⟩ (Dec. 3 2004).

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