Predicting Triaxial Compressive Strength and Young’s Modulus of Frozen Sand Using Artificial Intelligence Methods-Reference-Cited by-同舟云学术

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Predicting Triaxial Compressive Strength and Young’s Modulus of Frozen Sand Using Artificial Intelligence Methods

Published:2019-09 Issue:3 Volume:33 Page:04019007
ISSN:0887-381X
Container-title:Journal of Cold Regions Engineering
language:en
Short-container-title:J. Cold Reg. Eng.

Author:

Esmaeili-Falak Mahzad¹,Katebi Hooshang²,Vadiati Meysam³,Adamowski Jan⁴

Affiliation:

1. Lecturer, Dept. of Geotechnical Engineering, Faculty of Civil Engineering, Univ. of Tabriz, 29 Bahman Blvd., Tabriz, Iran.

2. Professor, Dept. of Geotechnical Engineering, Faculty of Civil Engineering, Univ. of Tabriz, 29 Bahman Blvd., Tabriz, Iran.

3. Visiting Researcher, Dept. of Bioresource Engineering, McGill Univ., Sainte-Anne-de-Bellevue, QC, Canada H9X 3V9.

4. Professor, Dept. of Bioresource Engineering, McGill Univ., Sainte-Anne-de-Bellevue, QC, Canada H9X 3V9 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Industrial and Manufacturing Engineering,Geotechnical Engineering and Engineering Geology

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CR.1943-5495.0000188

Reference101 articles.

1. Shear moduli and damping in frozen and unfrozen clay by resonant column tests

2. The effects of polymers and fly ash on unconfined compressive strength and freeze-thaw behavior of loose saturated sand

3. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization

4. An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

5. Development of optimal fuzzy models for predicting the strength of intact rocks

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