Machine Learning–Based Organic Soil Classification Using Cone Penetrometer Tests-Reference-Cited by-同舟云学术

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Machine Learning–Based Organic Soil Classification Using Cone Penetrometer Tests

Published:2024-09 Issue:9 Volume:150 Page:
ISSN:1090-0241
Container-title:Journal of Geotechnical and Geoenvironmental Engineering
language:en
Short-container-title:J. Geotech. Geoenviron. Eng.

Author:

Ulloa H. Omar¹^ORCID,Ramirez Alex²,Jafari Navid H.³,Kameshwar Sabarethinam⁴^ORCID,Harrouch Ignacio⁵

Affiliation:

1. Project Engineer, Geopier Foundation Company, 130 Harbour Place Dr., Suite 280, Davidson, NC 28036 (corresponding author). ORCID: .

2. Geotechnical Engineer, Stantec, 1340 Poydras St., Suite 1420, New Orleans, LA 70112.

3. Associate Professor, Dept. of Civil and Environmental Engineering, Louisiana State Univ., 3212D Patrick F. Taylor Hall, Baton Rouge, LA 70803.

4. Assistant Professor, Dept. of Civil and Environmental Engineering, Louisiana State Univ., 3230L Patrick F. Taylor Hall, Baton Rouge, LA 70803. ORCID: .

5. Senior Principal, Stantec, 1200 Brickyard Lane, Suite 400, Baton Rouge, LA 70802.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JGGEFK.GTENG-12322

Reference46 articles.

1. CPTU identification of regular, sensitive, and organic clays towards evaluating preconsolidation stress profiles

2. Baligh, M. M., and J.-N. Levadoux. 1980. Pore pressure dissipation after cone penetration. Rep. No. MIT-T-80-003. Washington, DC: National Oceanic and Atmospheric Administration.

3. A random forest guided tour

4. Soil Classification System from Cone Penetration Test Data Applying Distance-Based Machine Learning Algorithms

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