Predicting Resilient Modulus of Cementitiously Stabilized Subgrade Soils Using Neural Network, Support Vector Machine, and Gaussian Process Regression-Reference-Cited by-同舟云学术

Predicting Resilient Modulus of Cementitiously Stabilized Subgrade Soils Using Neural Network, Support Vector Machine, and Gaussian Process Regression

Published:2021-06 Issue:6 Volume:21 Page:04021073
ISSN:1532-3641
Container-title:International Journal of Geomechanics
language:en
Short-container-title:Int. J. Geomech.

Author:

Hu Xi¹^ORCID,Solanki Pranshoo²

Affiliation:

1. Ph.D. Student, Dept. of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030. ORCID: .

2. Associate Professor, Dept. of Technology, Illinois State University, Campus Box 5100, Normal, IL 61780 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Soil Science

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GM.1943-5622.0002029

Reference62 articles.

1. Automatic kernel selection for gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo;Abdessalem A. B.;Front. Built. Environ.,2017

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