Development of Evaluation Framework for the Unconfined Compressive Strength of Soils Based on the Fundamental Soil Parameters Using Gene Expression Programming and Deep Learning Methods-Reference-Cited by-同舟云学术

Development of Evaluation Framework for the Unconfined Compressive Strength of Soils Based on the Fundamental Soil Parameters Using Gene Expression Programming and Deep Learning Methods

Published:2022-02 Issue:2 Volume:34 Page:
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Al Bodour Wassel¹^ORCID,Hanandeh Shadi²,Hajij Mustafa³,Murad Yasmin⁴^ORCID

Affiliation:

1. Assistant Professor, Dept. of Civil Engineering, Univ. of Jordan, Amman 11942, Jordan (corresponding author). ORCID: .

2. Assistant Professor, Dept. of Civil Engineering, Al-Balqa’ Applied Univ., Salt 11942, Jordan.

3. Assistant Professor, Dept. of Mathematics and Computer Science, Santa Clara Univ., Santa Clara, CA 95053.

4. Associate Professor, Dept. of Civil Engineering, Univ. of Jordan, Amman 11942, Jordan. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29MT.1943-5533.0004087

Reference38 articles.

1. A robust data mining approach for formulation of geotechnical engineering systems

2. Fundamental Shear-Strength Properties of the Lilla Edet Clay

3. Undrained Strength Interrelationships among CIUC, UU, and UC Tests

4. Eid H. T. and T. D. Stark. 1998. “Undrained shear strength from cone penetration test.” In Vol. 98 of Proc. 1st Int. Conf. on Site Characterization—ISC edited by P. K. Robertson and P. W. Mayne 1021–1025. Rotterdam Netherlands: A.A. Balkema.

5. Automatically Defined Functions in Gene Expression Programming

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